Practical options for selecting data-driven or physics-based prognostics algorithms with reviews
This paper is to provide practical options for prognostics so that beginners can select appropriate methods for their fields of application. To achieve this goal, several popular algorithms are first reviewed in the data-driven and physics-based prognostics methods. Each algorithm’s attributes and p...
Saved in:
| Published in: | Reliability engineering & system safety Vol. 133; pp. 223 - 236 |
|---|---|
| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Oxford
Elsevier Ltd
01.01.2015
Elsevier |
| Subjects: | |
| ISSN: | 0951-8320, 1879-0836 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | This paper is to provide practical options for prognostics so that beginners can select appropriate methods for their fields of application. To achieve this goal, several popular algorithms are first reviewed in the data-driven and physics-based prognostics methods. Each algorithm’s attributes and pros and cons are analyzed in terms of model definition, model parameter estimation and ability to handle noise and bias in data. Fatigue crack growth examples are then used to illustrate the characteristics of different algorithms. In order to suggest a suitable algorithm, several studies are made based on the number of data sets, the level of noise and bias, availability of loading and physical models, and complexity of the damage growth behavior. Based on the study, it is concluded that the Gaussian process is easy and fast to implement, but works well only when the covariance function is properly defined. The neural network has the advantage in the case of large noise and complex models but only with many training data sets. The particle filter and Bayesian method are superior to the former methods because they are less affected by noise and model complexity, but work only when physical model and loading conditions are available.
•Practical review of data-driven and physics-based prognostics are provided.•As common prognostics algorithms, NN, GP, PF and BM are introduced.•Algorithms’ attributes, pros and cons, and applicable conditions are discussed.•This will be helpful to choose the best algorithm for different applications. |
|---|---|
| AbstractList | This paper is to provide practical options for prognostics so that beginners can select appropriate methods for their fields of application. To achieve this goal, several popular algorithms are first reviewed in the data-driven and physics-based prognostics methods. Each algorithm’s attributes and pros and cons are analyzed in terms of model definition, model parameter estimation and ability to handle noise and bias in data. Fatigue crack growth examples are then used to illustrate the characteristics of different algorithms. In order to suggest a suitable algorithm, several studies are made based on the number of data sets, the level of noise and bias, availability of loading and physical models, and complexity of the damage growth behavior. Based on the study, it is concluded that the Gaussian process is easy and fast to implement, but works well only when the covariance function is properly defined. The neural network has the advantage in the case of large noise and complex models but only with many training data sets. The particle filter and Bayesian method are superior to the former methods because they are less affected by noise and model complexity, but work only when physical model and loading conditions are available.
•Practical review of data-driven and physics-based prognostics are provided.•As common prognostics algorithms, NN, GP, PF and BM are introduced.•Algorithms’ attributes, pros and cons, and applicable conditions are discussed.•This will be helpful to choose the best algorithm for different applications. This paper is to provide practical options for prognostics so that beginners can select appropriate methods for their fields of application. To achieve this goal, several popular algorithms are first reviewed in the data-driven and physics-based prognostics methods. Each algorithm's attributes and pros and cons are analyzed in terms of model definition, model parameter estimation and ability to handle noise and bias in data. Fatigue crack growth examples are then used to illustrate the characteristics of different algorithms. In order to suggest a suitable algorithm, several studies are made based on the number of data sets, the level of noise and bias, availability of loading and physical models, and complexity of the damage growth behavior. Based on the study, it is concluded that the Gaussian process is easy and fast to implement, but works well only when the covariance function is properly defined. The neural network has the advantage in the case of large noise and complex models but only with many training data sets. The particle filter and Bayesian method are superior to the former methods because they are less affected by noise and model complexity, but work only when physical model and loading conditions are available. |
| Author | Choi, Joo-Ho Kim, Nam H. An, Dawn |
| Author_xml | – sequence: 1 givenname: Dawn surname: An fullname: An, Dawn email: dawnan@ufl.edu organization: Dept. of Mechanical & Aerospace Engineering, University of Florida, Gainesville, FL 32611, USA – sequence: 2 givenname: Nam H. surname: Kim fullname: Kim, Nam H. email: nkim@ufl.edu organization: Dept. of Mechanical & Aerospace Engineering, University of Florida, Gainesville, FL 32611, USA – sequence: 3 givenname: Joo-Ho surname: Choi fullname: Choi, Joo-Ho email: jhchoi@kau.ac.kr organization: Dept. of Aerospace & Mechanical Engineering, Korea Aerospace University, Goyang-si, Gyeonggi-do 412-791, Republic of Korea |
| BackLink | http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=28887268$$DView record in Pascal Francis |
| BookMark | eNp9kU1LJDEQhoO44OjuH9hTLgt76Tbp9EcCexFZXUHQg55jTVI9ZuhJxlSr-O_NMLIHD55eqLxPSD05ZocxRWTspxS1FLI_XdcZiepGyLYWpi5xwBZSD6YSWvWHbCFMJyutGnHEjonWQojWdMOCPdxmcHNwMPG0nUOKxMeUOeGEZRxX3MMMlc_hBSMvB9vHNwqOqiUQer7NaRUTFZ44TKuUw_y4If5agmd8CfhK39m3ESbCHx95wu4v_t6d_6uuby6vzs-uK6d6NVfYaum0bmDozVKi96AGo9HrQS2dMKN0sjNSqN47kKaMXVu26Rr0sPSNH9UJ-72_t7zp6RlptptADqcJIqZnsrLvZNu1nVal-uujClQWHzNEF8huc9hAfrON1npoel16zb7nciLKOP6vSGF32u3a7rTbnXYrjC1RIP0JcmGGndk5Q5i-Rv_sUSyeirxsyQWMDn3I5TesT-Er_B3iHKJs |
| CitedBy_id | crossref_primary_10_1016_j_apacoust_2018_04_005 crossref_primary_10_1016_j_actaastro_2018_10_029 crossref_primary_10_1016_j_ress_2024_110121 crossref_primary_10_1016_j_jclepro_2016_10_185 crossref_primary_10_1016_j_autcon_2024_105546 crossref_primary_10_1061__ASCE_PS_1949_1204_0000340 crossref_primary_10_1016_j_ymssp_2018_03_003 crossref_primary_10_1016_j_jmsy_2020_07_008 crossref_primary_10_1109_TII_2018_2793246 crossref_primary_10_1016_j_ijhydene_2023_08_098 crossref_primary_10_1109_TR_2019_2957965 crossref_primary_10_1088_1742_6596_2386_1_012027 crossref_primary_10_1088_2631_8695_ad68c9 crossref_primary_10_1016_j_ress_2019_03_044 crossref_primary_10_1109_JPHOTOV_2023_3272071 crossref_primary_10_1088_1361_6501_ad25dc crossref_primary_10_1016_j_procir_2020_03_080 crossref_primary_10_1016_j_ress_2024_110493 crossref_primary_10_1016_j_ress_2019_02_002 crossref_primary_10_3390_app10020467 crossref_primary_10_1016_j_ast_2019_105423 crossref_primary_10_1016_j_ress_2017_11_020 crossref_primary_10_1016_j_coche_2021_100671 crossref_primary_10_1007_s00170_021_08275_w crossref_primary_10_1016_j_ijfatigue_2021_106352 crossref_primary_10_1016_j_ress_2021_108203 crossref_primary_10_1016_j_rser_2021_111903 crossref_primary_10_1016_j_ces_2021_117354 crossref_primary_10_1109_TR_2017_2727489 crossref_primary_10_1016_j_measurement_2023_114082 crossref_primary_10_1088_2515_7639_abca7b crossref_primary_10_1093_jcde_qwad032 crossref_primary_10_1016_j_ijmecsci_2025_110723 crossref_primary_10_1007_s10916_020_1534_8 crossref_primary_10_1109_TAI_2023_3248561 crossref_primary_10_1109_ACCESS_2022_3214320 crossref_primary_10_1016_j_ymssp_2019_106315 crossref_primary_10_1016_j_ijfatigue_2023_107536 crossref_primary_10_1016_j_ijfatigue_2023_107658 crossref_primary_10_1016_j_ress_2017_02_007 crossref_primary_10_1177_14759217221127252 crossref_primary_10_1002_bate_202300105 crossref_primary_10_1016_j_measurement_2021_110393 crossref_primary_10_1016_j_rser_2019_109405 crossref_primary_10_1155_2018_7396293 crossref_primary_10_1016_j_asoc_2022_109630 crossref_primary_10_1016_j_jmsy_2023_10_010 crossref_primary_10_1109_JSEN_2023_3337365 crossref_primary_10_1016_j_asoc_2020_106665 crossref_primary_10_1016_j_microrel_2023_114914 crossref_primary_10_1016_j_cie_2024_110744 crossref_primary_10_1017_aer_2025_25 crossref_primary_10_1016_j_cie_2019_106206 crossref_primary_10_1109_TIE_2021_3050382 crossref_primary_10_1002_qre_3288 crossref_primary_10_1016_j_ress_2025_110893 crossref_primary_10_3390_electronics9101711 crossref_primary_10_1016_j_autcon_2023_104860 crossref_primary_10_1016_j_compchemeng_2017_11_006 crossref_primary_10_1061__ASCE_EY_1943_7897_0000405 crossref_primary_10_1016_j_microrel_2017_03_026 crossref_primary_10_1016_j_ress_2022_108922 crossref_primary_10_1177_1475921720933155 crossref_primary_10_1016_j_ijhydene_2024_06_090 crossref_primary_10_1016_j_ress_2020_107194 crossref_primary_10_1016_j_ress_2022_108482 crossref_primary_10_1016_j_measurement_2022_112085 crossref_primary_10_1177_00219983211037048 crossref_primary_10_1007_s10845_016_1221_2 crossref_primary_10_1016_j_eswa_2021_115627 crossref_primary_10_1109_TIM_2022_3209417 crossref_primary_10_1007_s40684_018_0055_0 crossref_primary_10_1016_j_scs_2019_101485 crossref_primary_10_1016_j_ress_2020_107147 crossref_primary_10_1016_j_measurement_2025_118070 crossref_primary_10_1177_16878132231215187 crossref_primary_10_1016_j_ress_2018_07_004 crossref_primary_10_2514_1_J060979 crossref_primary_10_1016_j_engappai_2021_104552 crossref_primary_10_1109_TR_2015_2500681 crossref_primary_10_1155_2022_9895907 crossref_primary_10_1016_j_aei_2020_101071 crossref_primary_10_1016_j_ymssp_2018_12_008 crossref_primary_10_1080_00401706_2025_2539785 crossref_primary_10_1109_ACCESS_2019_2935470 crossref_primary_10_1109_ACCESS_2024_3517705 crossref_primary_10_3390_su14159464 crossref_primary_10_1109_TPEL_2016_2618422 crossref_primary_10_1002_cjce_25460 crossref_primary_10_1016_j_engfracmech_2023_109478 crossref_primary_10_1007_s11356_021_18033_w crossref_primary_10_1016_j_ress_2024_110385 crossref_primary_10_1109_TR_2016_2570542 crossref_primary_10_1142_S0218539325500068 crossref_primary_10_1002_qre_2947 crossref_primary_10_1016_j_measurement_2019_107097 crossref_primary_10_1016_j_ress_2025_110906 crossref_primary_10_1109_ACCESS_2022_3211258 crossref_primary_10_1016_j_ress_2021_107683 crossref_primary_10_1007_s10462_022_10260_y crossref_primary_10_1007_s10845_024_02461_9 crossref_primary_10_1016_j_ymssp_2018_12_039 crossref_primary_10_1051_e3sconf_20184301020 crossref_primary_10_1016_j_ymssp_2024_111992 crossref_primary_10_1007_s00158_021_02868_5 crossref_primary_10_15302_J_FEM_2018050 crossref_primary_10_1016_j_jsv_2022_117276 crossref_primary_10_1016_j_ress_2018_03_022 crossref_primary_10_1088_1361_6501_aa56c9 crossref_primary_10_3390_technologies9010018 crossref_primary_10_1109_ACCESS_2019_2906388 crossref_primary_10_1177_1748006X221087503 crossref_primary_10_1016_j_jobe_2023_107376 crossref_primary_10_1016_j_engfracmech_2023_109242 crossref_primary_10_1016_j_ymssp_2019_106486 crossref_primary_10_1016_j_ymssp_2021_108599 crossref_primary_10_1016_j_measurement_2020_107950 crossref_primary_10_1108_EL_09_2023_0208 crossref_primary_10_1016_j_ress_2017_01_023 crossref_primary_10_1177_09544062241286372 crossref_primary_10_1016_j_ymssp_2022_109747 crossref_primary_10_1109_JSEN_2025_3592180 crossref_primary_10_1002_lpor_202000254 crossref_primary_10_1016_j_ymssp_2023_110910 crossref_primary_10_3390_machines10020072 crossref_primary_10_1177_1475921720971551 crossref_primary_10_1080_01430750_2022_2056917 crossref_primary_10_1007_s10845_018_1453_4 crossref_primary_10_1016_j_ymssp_2020_106987 crossref_primary_10_1016_j_ymssp_2022_108805 crossref_primary_10_1002_qre_2001 crossref_primary_10_1016_j_ress_2021_107746 crossref_primary_10_1109_TR_2020_3002262 crossref_primary_10_32604_sdhm_2022_016905 crossref_primary_10_1007_s12206_018_0507_z crossref_primary_10_1016_j_asoc_2022_109570 crossref_primary_10_1016_j_ress_2021_107740 crossref_primary_10_1016_j_ress_2020_107225 crossref_primary_10_1016_j_oceaneng_2025_122286 crossref_primary_10_1007_s41062_022_00888_8 crossref_primary_10_1121_1_5129076 crossref_primary_10_3390_electronics12020312 crossref_primary_10_1109_ACCESS_2023_3243432 crossref_primary_10_1016_j_ymssp_2022_108917 crossref_primary_10_1177_1748006X18764061 crossref_primary_10_1016_j_apenergy_2020_115338 crossref_primary_10_1016_j_engfracmech_2023_109103 crossref_primary_10_1109_ACCESS_2021_3118283 crossref_primary_10_1016_j_ress_2021_107758 crossref_primary_10_1016_j_aei_2019_100977 crossref_primary_10_1016_j_ress_2021_108048 crossref_primary_10_1088_1361_665X_abdd00 crossref_primary_10_2514_1_G004616 crossref_primary_10_1007_s12541_021_00513_1 crossref_primary_10_3390_s23156860 crossref_primary_10_1016_j_cpc_2020_107256 crossref_primary_10_1016_j_measurement_2022_110939 crossref_primary_10_1016_j_rser_2017_06_002 crossref_primary_10_1051_matecconf_20167400005 crossref_primary_10_1017_pds_2022_141 crossref_primary_10_1177_1687814016685963 crossref_primary_10_1007_s00170_022_09291_0 crossref_primary_10_1016_j_egyr_2020_11_265 crossref_primary_10_1088_1361_6501_abbe3b crossref_primary_10_1016_j_advengsoft_2023_103461 crossref_primary_10_3390_app13084655 crossref_primary_10_1007_s13349_020_00434_z crossref_primary_10_1109_ACCESS_2021_3135511 crossref_primary_10_1016_j_ymssp_2016_01_010 crossref_primary_10_1016_j_procs_2021_05_033 crossref_primary_10_3389_fenrg_2021_696785 crossref_primary_10_1016_j_ymssp_2017_01_050 crossref_primary_10_3390_rs14143357 crossref_primary_10_1007_s11771_020_4450_7 crossref_primary_10_1016_j_isatra_2020_09_017 crossref_primary_10_1007_s11694_024_02533_7 crossref_primary_10_1016_j_ces_2021_116571 crossref_primary_10_1109_ACCESS_2019_2911307 crossref_primary_10_1109_JSEN_2022_3145194 crossref_primary_10_1017_aer_2024_101 crossref_primary_10_1088_1361_6501_ad8947 crossref_primary_10_1007_s12206_020_1230_0 crossref_primary_10_1016_j_arcontrol_2021_04_001 crossref_primary_10_3390_fractalfract8050293 crossref_primary_10_1109_TIM_2024_3470018 crossref_primary_10_1016_j_ress_2018_06_021 crossref_primary_10_3390_s23198124 crossref_primary_10_1007_s00500_019_04311_w crossref_primary_10_1016_j_ress_2016_09_005 crossref_primary_10_1016_j_ress_2017_04_005 crossref_primary_10_1088_1361_6501_ac2fe8 crossref_primary_10_1016_j_ress_2023_109479 crossref_primary_10_1016_j_compchemeng_2016_08_018 crossref_primary_10_1016_j_ress_2017_09_021 crossref_primary_10_3390_ma15217797 crossref_primary_10_1016_j_ress_2021_108063 crossref_primary_10_1002_tcr_202200131 crossref_primary_10_1016_j_ress_2017_05_042 crossref_primary_10_1016_j_ress_2024_110515 crossref_primary_10_1016_j_ress_2024_110517 crossref_primary_10_1002_qre_2688 crossref_primary_10_1109_TIM_2017_2735661 crossref_primary_10_3390_aerospace8050129 crossref_primary_10_1177_1748006X221147441 crossref_primary_10_3390_en9110896 crossref_primary_10_1016_j_advengsoft_2018_02_006 crossref_primary_10_1038_s41598_024_67259_4 crossref_primary_10_3390_s21227655 crossref_primary_10_1109_JIOT_2023_3272535 crossref_primary_10_1109_TR_2017_2691730 crossref_primary_10_1177_1748006X221132870 crossref_primary_10_1177_0954406217734885 crossref_primary_10_1016_j_jmsy_2024_08_025 crossref_primary_10_1016_j_scs_2019_101886 crossref_primary_10_1016_j_cja_2017_02_005 crossref_primary_10_1038_s41598_025_98845_9 crossref_primary_10_1016_j_neucom_2018_09_076 crossref_primary_10_1016_j_taml_2023_100440 crossref_primary_10_3390_su13126828 crossref_primary_10_1002_stc_2969 crossref_primary_10_3390_app12168071 crossref_primary_10_1155_2023_1091276 crossref_primary_10_1109_TR_2019_2907402 crossref_primary_10_1007_s41403_022_00342_2 crossref_primary_10_1109_ACCESS_2019_2948291 crossref_primary_10_1016_j_ejor_2018_02_033 crossref_primary_10_3390_app13137706 crossref_primary_10_1016_j_enbuild_2024_114898 crossref_primary_10_3390_s19051070 crossref_primary_10_3390_su13158548 crossref_primary_10_1016_j_apm_2023_05_042 crossref_primary_10_1016_j_paerosci_2021_100758 crossref_primary_10_3390_jmse13071321 crossref_primary_10_3390_app122211725 crossref_primary_10_3390_aerospace11090741 crossref_primary_10_1002_cphc_202100829 crossref_primary_10_1016_j_engfailanal_2025_109683 crossref_primary_10_1016_j_jmsy_2023_02_019 crossref_primary_10_1002_adem_202201430 crossref_primary_10_1016_j_engappai_2023_105859 crossref_primary_10_3390_s18113909 crossref_primary_10_1016_j_aei_2021_101405 crossref_primary_10_1016_j_aei_2021_101404 crossref_primary_10_1088_1757_899X_806_1_012041 crossref_primary_10_3390_aerospace9120839 crossref_primary_10_1016_j_ress_2024_110666 crossref_primary_10_1016_j_aei_2023_102094 crossref_primary_10_1016_j_compchemeng_2022_107669 crossref_primary_10_1016_j_ymssp_2023_111037 crossref_primary_10_3390_app11083380 crossref_primary_10_1016_j_rser_2025_115408 crossref_primary_10_3390_machines11020211 crossref_primary_10_1016_j_asoc_2020_106628 crossref_primary_10_3389_fenrg_2022_991343 crossref_primary_10_1016_j_compind_2016_12_008 crossref_primary_10_1109_JSEN_2024_3411818 crossref_primary_10_1007_s12046_021_01582_8 crossref_primary_10_1109_JSEN_2020_2979797 crossref_primary_10_1109_TIM_2015_2427891 crossref_primary_10_1109_TIM_2023_3251391 crossref_primary_10_1016_j_ress_2022_108322 crossref_primary_10_1016_j_ultras_2017_07_016 crossref_primary_10_3390_app10041323 crossref_primary_10_3390_pr12050849 crossref_primary_10_1007_s00500_022_07129_1 crossref_primary_10_1016_j_ijcip_2020_100391 crossref_primary_10_1631_FITEE_2100580 crossref_primary_10_1080_19401493_2019_1597924 crossref_primary_10_1109_TIM_2024_3481588 crossref_primary_10_3390_act14080382 |
| Cites_doi | 10.1016/j.jpowsour.2011.03.101 10.36001/phmconf.2010.v2i1.1753 10.1016/j.aei.2004.08.001 10.1155/2013/425740 10.1214/ss/1177012413 10.1109/ICIINFS.2008.4798417 10.1016/j.ymssp.2005.09.012 10.1016/0893-6080(95)00144-1 10.2307/1270528 10.1016/j.renene.2013.06.025 10.2514/6.2012-1435 10.1016/j.wear.2011.02.010 10.1111/j.1467-9868.2011.01007.x 10.2514/1.C031808 10.1016/j.eswa.2007.06.029 10.1098/rstl.1763.0053 10.1016/j.ijfatigue.2007.03.004 10.1007/s11063-009-9108-2 10.1177/0142331208092030 10.1109/PHM.2008.4711464 10.1109/TIM.2008.2005963 10.1007/s12206-013-0428-9 10.1109/ICSMC.2006.385301 10.1016/j.asoc.2012.08.031 10.1109/ICICISYS.2009.5358175 10.1007/s00158-012-0776-6 10.1016/j.procs.2013.05.057 10.1214/aoms/1177704472 10.1109/72.857781 10.1109/78.978383 10.1109/MIM.2013.6495676 10.1016/j.ejor.2010.11.018 10.1080/00949655.2013.848452 10.1117/12.469869 10.1115/1.3662552 10.5391/IJFIS.2007.7.4.221 10.1109/AERO.2011.5747574 10.1016/j.probengmech.2010.07.010 10.1016/j.ijhydene.2014.05.005 10.1109/TR.2014.2299152 10.1177/1045389X08099602 10.1080/00949659708811843 10.1115/1.3656900 10.1162/neco.1994.6.6.1289 10.1109/72.478409 10.1016/S0893-6080(05)80155-8 10.1016/j.ymssp.2012.08.016 10.1049/ip-cds:20040495 10.1080/10618600.1994.10474644 10.1016/j.mineng.2013.05.026 10.1111/1467-9868.00280 10.1016/S0169-7439(97)00061-0 10.1016/j.ymssp.2006.02.009 10.1007/s11222-010-9224-x 10.1016/j.ress.2013.02.019 10.1016/j.hbrcj.2013.04.001 10.1016/S0951-8320(01)00148-X 10.1016/S0893-6080(05)80092-9 10.1016/j.csda.2012.04.020 10.1016/j.ymssp.2012.05.004 10.1109/IJCNN.2001.938431 10.1016/0893-6080(90)90049-Q 10.1109/NAFIPS.2005.1548498 10.1016/j.jsv.2004.02.058 10.1109/TSMCB.2003.811293 10.1046/j.1365-8711.2003.06271.x 10.1016/j.apenergy.2014.01.066 10.1016/j.rser.2013.07.026 10.1016/j.engappai.2014.02.009 10.3969/j.issn.1004-4132.2011.03.010 10.1016/j.cma.2013.07.011 10.2514/1.J051268 10.1162/neco.1995.7.5.867 10.1177/1475921711424520 10.1016/j.ymssp.2013.06.004 10.1016/j.renene.2011.06.023 10.1016/j.ress.2009.08.001 10.1142/S0129065704001899 10.5539/eer.v1n1p81 10.1016/j.ymssp.2011.01.007 10.1016/j.ress.2005.11.035 10.1016/j.ress.2012.11.011 10.1016/0890-6955(94)90083-3 10.1016/S0893-6080(99)00080-5 10.1023/A:1020281327116 10.1109/TNN.2011.2162110 10.1109/ICMTMA.2011.337 10.1016/j.neucom.2013.05.024 10.1016/j.apenergy.2013.05.075 10.36001/phmconf.2010.v2i1.1896 10.1088/0954-898X/8/3/004 10.1057/jos.2012.20 10.1016/j.csda.2004.02.006 10.1016/j.microrel.2012.12.004 |
| ContentType | Journal Article |
| Copyright | 2014 Elsevier Ltd 2015 INIST-CNRS |
| Copyright_xml | – notice: 2014 Elsevier Ltd – notice: 2015 INIST-CNRS |
| DBID | AAYXX CITATION IQODW 7TB 8FD FR3 |
| DOI | 10.1016/j.ress.2014.09.014 |
| DatabaseName | CrossRef Pascal-Francis Mechanical & Transportation Engineering Abstracts Technology Research Database Engineering Research Database |
| DatabaseTitle | CrossRef Technology Research Database Mechanical & Transportation Engineering Abstracts Engineering Research Database |
| DatabaseTitleList | Technology Research Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Mathematics Applied Sciences Statistics Physics |
| EISSN | 1879-0836 |
| EndPage | 236 |
| ExternalDocumentID | 28887268 10_1016_j_ress_2014_09_014 S0951832014002245 |
| GroupedDBID | --K --M .~1 0R~ 123 1B1 1~. 1~5 29P 4.4 457 4G. 5VS 7-5 71M 8P~ 9JN 9JO AABNK AACTN AAEDT AAEDW AAFJI AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AAXUO ABEFU ABFNM ABJNI ABMAC ABMMH ABTAH ABXDB ABYKQ ACDAQ ACGFS ACIWK ACNNM ACRLP ADBBV ADEZE ADMUD ADTZH AEBSH AECPX AEKER AENEX AFKWA AFRAH AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AIEXJ AIKHN AITUG AJBFU AJOXV AKYCK ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOMHK ASPBG AVARZ AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q GBLVA HVGLF HZ~ IHE J1W JJJVA KOM LY7 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. PRBVW Q38 R2- RIG ROL RPZ SDF SDG SES SET SEW SPC SPCBC SSB SSO SST SSZ T5K TN5 WUQ XPP ZMT ZY4 ~G- 9DU AATTM AAXKI AAYWO AAYXX ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION EFKBS ~HD BNPGV IQODW SSH 7TB 8FD FR3 |
| ID | FETCH-LOGICAL-c363t-e481c882a769b1edda3798ed873bc09f1c1591036dca19d87c483252edabd2df3 |
| ISICitedReferencesCount | 342 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000345469600022&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0951-8320 |
| IngestDate | Mon Sep 29 04:33:34 EDT 2025 Wed Apr 02 07:08:22 EDT 2025 Sat Nov 29 07:56:38 EST 2025 Tue Nov 18 22:33:02 EST 2025 Fri Feb 23 02:28:03 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Physics-based prognostics Gaussian process regression Neural network Bayesian inference Data-driven prognostics Particle filter Availability Bayes estimation Monte Carlo method Parameter estimation Contour line Regression analysis Review Data driven modelling Modeling Fatigue crack Noise level Crack propagation Particle method Gaussian process Model matching Covariance Physical model Damaging |
| Language | English |
| License | CC BY 4.0 |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c363t-e481c882a769b1edda3798ed873bc09f1c1591036dca19d87c483252edabd2df3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| PQID | 1651454583 |
| PQPubID | 23500 |
| PageCount | 14 |
| ParticipantIDs | proquest_miscellaneous_1651454583 pascalfrancis_primary_28887268 crossref_primary_10_1016_j_ress_2014_09_014 crossref_citationtrail_10_1016_j_ress_2014_09_014 elsevier_sciencedirect_doi_10_1016_j_ress_2014_09_014 |
| PublicationCentury | 2000 |
| PublicationDate | January 2015 2015-01-00 2015 20150101 |
| PublicationDateYYYYMMDD | 2015-01-01 |
| PublicationDate_xml | – month: 01 year: 2015 text: January 2015 |
| PublicationDecade | 2010 |
| PublicationPlace | Oxford |
| PublicationPlace_xml | – name: Oxford |
| PublicationTitle | Reliability engineering & system safety |
| PublicationYear | 2015 |
| Publisher | Elsevier Ltd Elsevier |
| Publisher_xml | – name: Elsevier Ltd – name: Elsevier |
| References | Paciorek, Schervish (bib84) 2004; vol. 16 Bediaga, Mendizabal, Arnaiz, Munoa (bib10) 2013; 16 Xing, Y, Miao, Q, Tsui, K-L, Pecht, M., Prognostics and health monitoring for lithium-ion battery. 2011 In: IEEE international conference, pp. 242–247. Guan, X, Liu, Y, Saxena, A, Celaya, J, Goebel, K., Entropy-based probabilistic fatigue damage prognosis and algorithmic performance comparison. In: Annual conference of the prognostics and health management society, San Diego, CA; September 27–October 1, 2009. Zhang, Lee (bib17) 2011; 196 Rumelhart, Hinton, Williams (bib38) 1986; vol. 1 Liu, D, Pang, J, Zhou, J, Peng, Y., Data-driven prognostics for lithium-ion battery based on Gaussian process regression, 2012. In: Prognostics and system health management conference, Beijing, China; May 23–25, 2012. Si, Wang, Hu, Chen, Zhou (bib32) 2013; 35 Jacobs (bib60) 1995; 7 Nawi, Ransing, Ransing (bib58) 2008; 4 Freitas, de JFG., “Bayesian methods for neural networks.” PhD thesis. University of Cambridge, UK, 2003. Yan, Liu, Han, Qiu (bib28) 2013; 27 Santner, Williams, Notz (bib80) 2003 Tang, Song, Li, Deng (bib4) 2014; 62 Choi JH, An D, Gang J, Joo J, Kim NH. Bayesian approach for parameter estimation in the structural analysis and prognosis. In: Proceedings of the annual conference of the prognostics and health management society, Portland, OR; October 13–16, 2010. Daigle, M, Goebel, K., Multiple damage progression paths in model-based prognostics. In: IEEE Aerospace conference, Big Sky, Montana; 2011. Lee, Wu, Zhao, Ghaffari, Liao, Siegel (bib14) 2014; 42 He, Tan, Sun (bib42) 2004; 151 Merwe, Rvd, Doucet, A, Freitas, N. de, Wan, E., The unscented particle filter. In: NIPS; 2000, pp. 584–590. Bodén (bib39) 2002 Chang, Hsieh (bib51) 2011; 7 Giurgiutiu V., Current issues in vibration-based fault diagnostics and prognostics. In: SPIE’s ninth annual international symposium on smart structures and materials and seventh annual international symposium on NDE for health monitoring and diagnostics, San Diego, CA; March 17–21, 2002. Paris, Erdogan (bib129) 1963; 85 Specht (bib73) 1990; 3 Subudhi, B, Jena, D, Gupta, MM, Memetic differential evolution trained neural networks for nonlinear system identification. In: IEEE region 10 colloquium and the third international conference on industrial and information systems, Kharagpur, India; December 8–10, 2008. Ahmadzadeh, Lundberg (bib22) 2013; 53 . Yan, Gao (bib7) 2007; 21 Grall, Bérenguer, Dieulle (bib11) 2002; 76 Kunli M, Yunxin W. Fault diagnosis of rolling element bearing based on vibration frequency analysis. In: Proceedings of the third international conference on measuring technology and mechatronics automation, Shangshai, China; January 6–7, 2011. Drucker, Cortes, Jackel, LeCun, Vapnik (bib61) 1994; 6 Wang, WP, Liao, S, Xing, TW., Particle filter for state and parameter estimation in passive ranging. In: IEEE international conference on intelligent computing and intelligent systems. Shanghai, China; 2009. Chao, Zhi, Liu, Min (bib63) 2014; 32 Rubin (bib119) 1998; vol. 3 Gómez, Franco, Jérez (bib48) 2009; 30 Rasmussen, Williams (bib81) 2006 Bicciato, S, Pandin, M, Didonè, G, Bello, CD., Analysis of an associative memory neural network for pattern identification in gene expression data. In: Biokdd01, workshop on data mining in bioinformatics, pp. 22–30. Andrianakis, Challenor (bib96) 2012; 56 Mohanty, Das, Chattopadhyay, Peralta (bib83) 2009; 20 Liu, Li, Al-Khalifa, Hamouda, Coit, Elsayed (bib12) 2013; 45 Khosravi, Nahavandi, Creighton, Atiya (bib78) 2011; 22 Saha, Goebel, Christophersen (bib15) 2009; 31 An, Choi, Kim (bib122) 2012; 11 Soares, Antunes, Araújo (bib59) 2013; 121 Storvik (bib132) 2002; 50 Zhang, Yuen (bib45) 2013; 17 Mao, Tan, Ser (bib76) 2000; 11 Chryssoloiuris, Lee, Ramsey (bib67) 1996; 7 Zhang, Khawaja, Patrick, Vachtsevanos, Orchard, Saxena (bib126) 2009; 58 Andrieu, Freitas de, Doucet, Jordan (bib115) 2003; 50 Gelfand, Sahu (bib121) 1994; 3 Oden, Prudencio, Bauman (bib97) 2013; 266 Coppe, A, Haftka, RT, Kim, NH, Yuan, FG., Reducing uncertainty in damage growth properties by structural health monitoring. In: Annual conference of the prognostics and health management society, San Diego, CA; September 27–October 1, 2009. Yang, L, Kavli, T, Carlin, M, Clausen, S, Groot, PFM., An evaluation of confidence bound estimation methods for neural networks. In: European symposium on intelligent techniques; 2000, Aachen, Germany, September 14–15, 2000. Xing, Y, Williard, N, Tsui, K-L, Pecht, M., A comparative review of prognostics-based reliability methods for lithium batteries. In: Prognostics and system health management conference, Shenzhen, China; May 24–25, 2011. Ostafe, D, Neural network hidden layer number determination using pattern recognition techniques. In: Second Romanian-Hungarian joint symposium on applied computational intelligence, Timisoara, Romania; 2005. Jardine, Lin, Banjevic (bib1) 2006; 20 Deguchi, Kamimoto, Wang, Yan, Liu, Watanabe (bib5) 2014; 6 Kang, Zhao, Ma (bib54) 2014; 121 1997. Chen, SC, Lin, SW, Tseng, TY, Lin, HC, Optimization of back-propagation network using simulated annealing approach. In: IEEE international conference on systems, man, and cybernetics, Taipei, Taiwan; October 8–11, 2006. Sacks, Welch, Mitchell, Wynn (bib87) 1989; 4 Silva, Gouriveau, Jemeï, Hissel, Boulon, Agbossou (bib25) 2014; 39 Si, Wang, Hu, Zhou (bib13) 2011; 213 Khosravi, Nahavandi, Creighton (bib72) 2013; 112 Khawaja, T, Vachtsevanos, G, Wu, B., Reasoning about uncertainty in prognosis: a confidence prediction neural network approach, 2005. In: Annual meeting of the north American Fuzzy Information Processing Society, Ann Arbor, MI; June 22–25, 2005. Happel, Murre (bib44) 1994; 7 Wang, Balakrishnan, Guo, Jiang (bib31) 2013 MacKay, DJC., Gaussian processes-a replacement for supervised neural networks? Tutorial lecture notes for NIPS, UK Higuchi (bib111) 1997; 59 Coppe, Haftka, Kim (bib30) 2011; 49 Zio, Maio (bib24) 2010; 95 Kalman (bib102) 1960; 82 He, W, Williard, N, Osterman, M, Pecht, M., Prognostics of lithium-ion batteries using extended Kalman filtering. In: Proceedings of IMAPS advanced technology workshop on high reliability microelectronics for military applications, Linthicum Heights, MD, USA; May 2011, pp. 17–19. Xu, Xu (bib19) 2011; 22 Gilks, Berzuini (bib114) 2001; 63 Rivals, Personnaz (bib69) 2000; 13 Wilson AG, Adams RP. Gaussian process covariance kernels for pattern discovery and extrapolation. arXiv preprint arXiv:1302.4245; 2013. URL Liao, Köttig (bib18) 2014; 63 Wilamowski, BM, Iplikci, S, Kaynak, O, Efe, MÖ., An algorithm for fast convergence in training neural networks. In: Proceedings of the international joint conference on neural networks, Vol. 3; 2001. pp. 1778–1782. Orchard, Vachtsevanos (bib107) 2007; 7 An, D, Choi, JH, Kim, NH., A comparison study of methods for parameter estimation in the physics-based prognostics. In: 53rd AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics and materials conference, Honolulu, Hawaii; April 23–26, 2012. Salomon, Hemmen (bib55) 1996; 9 Seeger (bib27) 2004; 14 Doucet, De Freitas, Gordon (bib104) 2001 Rebba, Mahadevan, Huang (bib98) 2006; 91 Lawrence, Seeger, Herbrich (bib91) 2003; vol. 15 Sheela, Deepa (bib49) 2013; 2013 Sargent (bib99) 2013; 7 Kitagawa (bib112) 1987; 82 Coppe, Pais, Haftka, Kim (bib101) 2012; 49 Firth, Lahav, Somerville (bib37) 2003; 339 Ling, Mahadevan (bib100) 2013; 111 Gramacy, Lee (bib95) 2012; 22 Samuel, Pines (bib3) 2005; 282 Guo, Zhao, Lu, Wang (bib52) 2012; 37 Sugumaran, Sabareesh, Ramachandran (bib6) 2008; 34 Neal, RM., Bayesian learning for neural networks PhD thesis, University of Toronto, Ontario, Canada, 1995. Huang, Torgeir, Cui (bib130) 2008; 30 Martin (bib2) 1994; 34 An, Choi, Kim (bib110) 2013; 115 An, Choi (bib120) 2013; 2 Qiua, Lee, Linc, Yu (bib127) 2003; 17 Melkumyan, A, Ramos, F., “A sparse covariance function for exact Gaussian process inference in large datasets. In: IJCAI 2009, proceedings of the 21st International joint conference on artificial intelligence, Pasadena, CA., July 11–17; 2009, pp. 1936–1942. Li, Wang, Ismail (bib23) 2013; 13 Naftaly, Intrator, Horn (bib64) 1997; 8 Svozil, Kvasnička, Pospíchal (bib34) 1997; 39 Efron, Tibshirani (bib71) 1994 Kim, Park (bib116) 2011; 26 Miao, Xie, Cui, Liang, Pecht (bib118) 2013; 53 Gelman, Carlin, Stern, Rubin (bib79) 2004 Sang, Huang (bib94) 2012; 74 Bayes (bib106) 1763; 53 Rovithakis, Maniadakis, Zervakis (bib46) 2004; 34 Belhouari, Bermak (bib85) 2004; 47 Liu, Dong, Peng (bib33) 2012; 32 An, Choi, Schmitz, Kim (bib109) 2011; 270 Abouel-seoud, Elmorsy, Dyab (bib128) 2011; 1 Radzieński, Krawczuk, Palacz (bib9) 2011; 25 Benkedjouh, Medjaher, Zerhouni, Rechak (bib29) 2013; 19 Gu, J, Azarian, MH, Pecht, MG., Failure prognostics of multilayer ceramic capacitors in temperature-humidity-bias conditions 2008. In: International conference on prognostics and health management, Denver, Colorado; October 6–9, 2008. Duch, Jankowski (bib35) 1999; 2 Neal (bib89) 1998; vol. 6 Liu, Li (bib41) 2004 Veaux, Schumi, Schweinsberg, Ungar (bib68) 1998; 40 Williams (bib82) 1997; vol. 9 Chakraborty, Mehrotra, Mohan, Ranka (bib21) 1992; 5 Parzen (bib74) 1962; 33 Krogh, Vedelsby (bib62) 1995; vol. 7 Foster, Waagen, Aijaz, Hurley, Luis, Rinsky (bib92) 2009; 10 An, Choi (bib88) 2012; 46 Liu, J, Saxena, A, Goebel, K, Saha, B, Wang, W, An adaptive recurrent neural network for remaining useful life prediction of lithium-ion batteries. In: Annual conference of the prognostics and health management society, Portland, Oregon; October 10–16, 2010. Hodhod, Ahmed (bib53) 2013; 9 Lawrence, S, Giles, CL, Tsoi, AC., What size neural network gives optimal generalization? Convergence properties of backpropagation technical reports, UM computer science department, UMIACS; Octobor 15, 1998. 10.1016/j.ress.2014.09.014_bib50 Kalman (10.1016/j.ress.2014.09.014_bib102) 1960; 82 Yan (10.1016/j.ress.2014.09.014_bib28) 2013; 27 Zhang (10.1016/j.ress.2014.09.014_bib45) 2013; 17 Rebba (10.1016/j.ress.2014.09.014_bib98) 2006; 91 10.1016/j.ress.2014.09.014_bib57 10.1016/j.ress.2014.09.014_bib56 Salomon (10.1016/j.ress.2014.09.014_bib55) 1996; 9 Naftaly (10.1016/j.ress.2014.09.014_bib64) 1997; 8 Andrianakis (10.1016/j.ress.2014.09.014_bib96) 2012; 56 Zhang (10.1016/j.ress.2014.09.014_bib126) 2009; 58 Benkedjouh (10.1016/j.ress.2014.09.014_bib29) 2013; 19 Liu (10.1016/j.ress.2014.09.014_bib41) 2004 Sacks (10.1016/j.ress.2014.09.014_bib87) 1989; 4 Rasmussen (10.1016/j.ress.2014.09.014_bib81) 2006 Oden (10.1016/j.ress.2014.09.014_bib97) 2013; 266 Tang (10.1016/j.ress.2014.09.014_bib4) 2014; 62 Saha (10.1016/j.ress.2014.09.014_bib15) 2009; 31 Veaux (10.1016/j.ress.2014.09.014_bib68) 1998; 40 Sang (10.1016/j.ress.2014.09.014_bib94) 2012; 74 Qiua (10.1016/j.ress.2014.09.014_bib127) 2003; 17 Ahmadzadeh (10.1016/j.ress.2014.09.014_bib22) 2013; 53 10.1016/j.ress.2014.09.014_bib47 10.1016/j.ress.2014.09.014_bib43 Neal (10.1016/j.ress.2014.09.014_bib89) 1998; vol. 6 10.1016/j.ress.2014.09.014_bib40 Wang (10.1016/j.ress.2014.09.014_bib31) 2013 Si (10.1016/j.ress.2014.09.014_bib32) 2013; 35 Martin (10.1016/j.ress.2014.09.014_bib2) 1994; 34 Duch (10.1016/j.ress.2014.09.014_bib35) 1999; 2 Drucker (10.1016/j.ress.2014.09.014_bib61) 1994; 6 Radzieński (10.1016/j.ress.2014.09.014_bib9) 2011; 25 10.1016/j.ress.2014.09.014_bib36 An (10.1016/j.ress.2014.09.014_bib88) 2012; 46 Gelfand (10.1016/j.ress.2014.09.014_bib121) 1994; 3 Chao (10.1016/j.ress.2014.09.014_bib63) 2014; 32 Mao (10.1016/j.ress.2014.09.014_bib76) 2000; 11 An (10.1016/j.ress.2014.09.014_bib120) 2013; 2 Kitagawa (10.1016/j.ress.2014.09.014_bib112) 1987; 82 Krogh (10.1016/j.ress.2014.09.014_bib62) 1995; vol. 7 Gómez (10.1016/j.ress.2014.09.014_bib48) 2009; 30 10.1016/j.ress.2014.09.014_bib26 Seeger (10.1016/j.ress.2014.09.014_bib27) 2004; 14 Lawrence (10.1016/j.ress.2014.09.014_bib91) 2003; vol. 15 Gilks (10.1016/j.ress.2014.09.014_bib114) 2001; 63 Foster (10.1016/j.ress.2014.09.014_bib92) 2009; 10 Chryssoloiuris (10.1016/j.ress.2014.09.014_bib67) 1996; 7 Gelman (10.1016/j.ress.2014.09.014_bib79) 2004 Belhouari (10.1016/j.ress.2014.09.014_bib85) 2004; 47 Liao (10.1016/j.ress.2014.09.014_bib18) 2014; 63 Rovithakis (10.1016/j.ress.2014.09.014_bib46) 2004; 34 Specht (10.1016/j.ress.2014.09.014_bib73) 1990; 3 10.1016/j.ress.2014.09.014_bib20 Sargent (10.1016/j.ress.2014.09.014_bib99) 2013; 7 Li (10.1016/j.ress.2014.09.014_bib23) 2013; 13 Kim (10.1016/j.ress.2014.09.014_bib116) 2011; 26 Svozil (10.1016/j.ress.2014.09.014_bib34) 1997; 39 Rumelhart (10.1016/j.ress.2014.09.014_bib38) 1986; vol. 1 10.1016/j.ress.2014.09.014_bib16 Zio (10.1016/j.ress.2014.09.014_bib24) 2010; 95 An (10.1016/j.ress.2014.09.014_bib122) 2012; 11 Coppe (10.1016/j.ress.2014.09.014_bib101) 2012; 49 Ling (10.1016/j.ress.2014.09.014_bib100) 2013; 111 Andrieu (10.1016/j.ress.2014.09.014_bib115) 2003; 50 Mohanty (10.1016/j.ress.2014.09.014_bib83) 2009; 20 Kang (10.1016/j.ress.2014.09.014_bib54) 2014; 121 10.1016/j.ress.2014.09.014_bib93 10.1016/j.ress.2014.09.014_bib90 Chakraborty (10.1016/j.ress.2014.09.014_bib21) 1992; 5 Chang (10.1016/j.ress.2014.09.014_bib51) 2011; 7 An (10.1016/j.ress.2014.09.014_bib110) 2013; 115 Coppe (10.1016/j.ress.2014.09.014_bib30) 2011; 49 10.1016/j.ress.2014.09.014_bib131 Hodhod (10.1016/j.ress.2014.09.014_bib53) 2013; 9 Bayes (10.1016/j.ress.2014.09.014_bib106) 1763; 53 Orchard (10.1016/j.ress.2014.09.014_bib107) 2007; 7 10.1016/j.ress.2014.09.014_bib8 Paris (10.1016/j.ress.2014.09.014_bib129) 1963; 85 Bodén (10.1016/j.ress.2014.09.014_bib39) 2002 Khosravi (10.1016/j.ress.2014.09.014_bib78) 2011; 22 Huang (10.1016/j.ress.2014.09.014_bib130) 2008; 30 Si (10.1016/j.ress.2014.09.014_bib13) 2011; 213 10.1016/j.ress.2014.09.014_bib125 Liu (10.1016/j.ress.2014.09.014_bib12) 2013; 45 10.1016/j.ress.2014.09.014_bib123 Samuel (10.1016/j.ress.2014.09.014_bib3) 2005; 282 10.1016/j.ress.2014.09.014_bib124 Silva (10.1016/j.ress.2014.09.014_bib25) 2014; 39 Parzen (10.1016/j.ress.2014.09.014_bib74) 1962; 33 10.1016/j.ress.2014.09.014_bib86 Yan (10.1016/j.ress.2014.09.014_bib7) 2007; 21 Deguchi (10.1016/j.ress.2014.09.014_bib5) 2014; 6 Efron (10.1016/j.ress.2014.09.014_bib71) 1994 Jacobs (10.1016/j.ress.2014.09.014_bib60) 1995; 7 Rubin (10.1016/j.ress.2014.09.014_bib119) 1998; vol. 3 10.1016/j.ress.2014.09.014_bib70 Storvik (10.1016/j.ress.2014.09.014_bib132) 2002; 50 10.1016/j.ress.2014.09.014_bib117 Sugumaran (10.1016/j.ress.2014.09.014_bib6) 2008; 34 Sheela (10.1016/j.ress.2014.09.014_bib49) 2013; 2013 10.1016/j.ress.2014.09.014_bib113 10.1016/j.ress.2014.09.014_bib77 An (10.1016/j.ress.2014.09.014_bib109) 2011; 270 10.1016/j.ress.2014.09.014_bib75 Paciorek (10.1016/j.ress.2014.09.014_bib84) 2004; vol. 16 Santner (10.1016/j.ress.2014.09.014_bib80) 2003 Bediaga (10.1016/j.ress.2014.09.014_bib10) 2013; 16 Nawi (10.1016/j.ress.2014.09.014_bib58) 2008; 4 Higuchi (10.1016/j.ress.2014.09.014_bib111) 1997; 59 Lee (10.1016/j.ress.2014.09.014_bib14) 2014; 42 Miao (10.1016/j.ress.2014.09.014_bib118) 2013; 53 Soares (10.1016/j.ress.2014.09.014_bib59) 2013; 121 Jardine (10.1016/j.ress.2014.09.014_bib1) 2006; 20 Liu (10.1016/j.ress.2014.09.014_bib33) 2012; 32 Doucet (10.1016/j.ress.2014.09.014_bib104) 2001 Grall (10.1016/j.ress.2014.09.014_bib11) 2002; 76 Xu (10.1016/j.ress.2014.09.014_bib19) 2011; 22 10.1016/j.ress.2014.09.014_bib108 10.1016/j.ress.2014.09.014_bib105 10.1016/j.ress.2014.09.014_bib103 Abouel-seoud (10.1016/j.ress.2014.09.014_bib128) 2011; 1 Rivals (10.1016/j.ress.2014.09.014_bib69) 2000; 13 Happel (10.1016/j.ress.2014.09.014_bib44) 1994; 7 Khosravi (10.1016/j.ress.2014.09.014_bib72) 2013; 112 10.1016/j.ress.2014.09.014_bib66 10.1016/j.ress.2014.09.014_bib65 Firth (10.1016/j.ress.2014.09.014_bib37) 2003; 339 Zhang (10.1016/j.ress.2014.09.014_bib17) 2011; 196 Williams (10.1016/j.ress.2014.09.014_bib82) 1997; vol. 9 He (10.1016/j.ress.2014.09.014_bib42) 2004; 151 Gramacy (10.1016/j.ress.2014.09.014_bib95) 2012; 22 Guo (10.1016/j.ress.2014.09.014_bib52) 2012; 37 |
| References_xml | – volume: 17 start-page: 441 year: 2013 end-page: 448 ident: bib45 article-title: Toward a hybrid approach of primitive cognitive network process and particle swarm optimization neural network for forecasting publication-title: Procedia Comput Sci – volume: 20 start-page: 887 year: 2009 end-page: 896 ident: bib83 article-title: Gaussian process time series model for life prognosis of metallic structures publication-title: J Intell Mater Syst Struct – volume: 25 start-page: 2169 year: 2011 end-page: 2190 ident: bib9 article-title: Improvement of damage detection methods based on experimental modal parameters publication-title: Mech Syst Sig Process – reference: Merwe, Rvd, Doucet, A, Freitas, N. de, Wan, E., The unscented particle filter. In: NIPS; 2000, pp. 584–590. – volume: 6 year: 2014 ident: bib5 article-title: Applications of laser diagnostics to thermal power plants and engines publication-title: Appl Therm Eng – volume: 9 start-page: 15 year: 2013 end-page: 21 ident: bib53 article-title: Developing an artificial neural network model to evaluate chloride diffusivity in high performance concrete publication-title: HBRC J – volume: 7 start-page: 6837 year: 2011 end-page: 6847 ident: bib51 article-title: Particle swarm optimization based on back propagation network forecasting exchange rates publication-title: Int J Innovative Comput Inf Control – year: 2004 ident: bib41 article-title: Fuzzy neural network theory and application – volume: 282 start-page: 475 year: 2005 end-page: 508 ident: bib3 article-title: A review of vibration-based techniques for helicopter transmission diagnostics publication-title: J Sound Vib – reference: Coppe, A, Haftka, RT, Kim, NH, Yuan, FG., Reducing uncertainty in damage growth properties by structural health monitoring. In: Annual conference of the prognostics and health management society, San Diego, CA; September 27–October 1, 2009. – volume: 56 start-page: 4215 year: 2012 end-page: 4228 ident: bib96 article-title: The effect of the nugget on Gaussian process emulators of computer models publication-title: Comput Stat Data Anal – volume: 2 start-page: 1771 year: 2013 end-page: 1779 ident: bib120 article-title: Improved MCMC method for parameter estimation based on marginal probability density function publication-title: J Mech Sci Technol – volume: 5 start-page: 961 year: 1992 end-page: 970 ident: bib21 article-title: Forecasting the behavior of multivariate time series using neural networks publication-title: Neural Networks – volume: 49 start-page: 1965 year: 2012 end-page: 1973 ident: bib101 article-title: Using a simple crack growth model in predicting remaining useful life publication-title: J Aircr – reference: Xing, Y, Miao, Q, Tsui, K-L, Pecht, M., Prognostics and health monitoring for lithium-ion battery. 2011 In: IEEE international conference, pp. 242–247. – reference: Wilamowski, BM, Iplikci, S, Kaynak, O, Efe, MÖ., An algorithm for fast convergence in training neural networks. In: Proceedings of the international joint conference on neural networks, Vol. 3; 2001. pp. 1778–1782. – reference: Liu, D, Pang, J, Zhou, J, Peng, Y., Data-driven prognostics for lithium-ion battery based on Gaussian process regression, 2012. In: Prognostics and system health management conference, Beijing, China; May 23–25, 2012. – volume: 49 start-page: 2818 year: 2011 end-page: 2821 ident: bib30 article-title: Uncertainty identification of damage growth parameters using nonlinear regression publication-title: AIAA J – volume: 30 start-page: 71 year: 2009 end-page: 87 ident: bib48 article-title: Neural network architecture selection: can function complexity help? publication-title: Neural Proc Lett – volume: 8 start-page: 283 year: 1997 end-page: 296 ident: bib64 article-title: Optimal ensemble averaging of neural networks publication-title: Network Comput Neural Syst – volume: vol. 16 year: 2004 ident: bib84 article-title: Nonstationary covariance functions for Gaussian process regression publication-title: Advances in neural information processing systems – volume: 34 start-page: 527 year: 1994 end-page: 551 ident: bib2 article-title: A review by discussion of condition monitoring and fault diagnosis in machine tools publication-title: Int J Mach Tools Manuf – volume: 62 start-page: 1 year: 2014 end-page: 9 ident: bib4 article-title: Fault diagnosis for a wind turbine transmission system based on manifold learning and Shannon wavelet support vector machine publication-title: Renewable Energy – volume: 111 start-page: 217 year: 2013 end-page: 231 ident: bib100 article-title: Quantitative model validation techniques: new insights publication-title: Reliab EngSyst Saf – volume: 21 start-page: 824 year: 2007 end-page: 839 ident: bib7 article-title: Approximate entropy as a diagnostic tool for machine health monitoring publication-title: Mech Syst Sig Process – volume: 35 start-page: 219 year: 2013 end-page: 237 ident: bib32 article-title: A wiener process-based degradation model with a recursive filter algorithm for remaining useful life estimation publication-title: Mech Syst Sig Process – volume: 112 start-page: 120 year: 2013 end-page: 129 ident: bib72 article-title: Quantifying uncertainties of neural network-based electricity price forecasts publication-title: Appl Energy – volume: vol. 15 start-page: 625 year: 2003 end-page: 632 ident: bib91 article-title: Fast sparse Gaussian process methods: the information vector machine publication-title: Advances in neural information processing systems – reference: Daigle, M, Goebel, K., Multiple damage progression paths in model-based prognostics. In: IEEE Aerospace conference, Big Sky, Montana; 2011. – volume: 115 start-page: 161 year: 2013 end-page: 169 ident: bib110 article-title: Prognostics 101: a tutorial for particle filter-based prognostics algorithm using Matlab publication-title: Reliab EngSyst Saf – volume: 1 start-page: 81 year: 2011 end-page: 93 ident: bib128 article-title: Robust prognostics concept for gearbox with artificially induced gear crack utilizing acoustic emission publication-title: Energy Environ Res – volume: 213 start-page: 1 year: 2011 end-page: 14 ident: bib13 article-title: Remaining useful life estimation—a review on the statistical data driven approaches publication-title: Eur J Oper Res – volume: 91 start-page: 1390 year: 2006 end-page: 1397 ident: bib98 article-title: Validation and error estimation of computational models publication-title: Reliab Eng Syst Saf – volume: 63 start-page: 127 year: 2001 end-page: 146 ident: bib114 article-title: Following a moving target-Monte Carlo inference for dynamic Bayesian models publication-title: R Stat Soc B – volume: 339 start-page: 1195 year: 2003 ident: bib37 article-title: Estimating photometric redshifts with artificial neural networks publication-title: Mon Not R Astron Soc – volume: 30 start-page: 2 year: 2008 end-page: 10 ident: bib130 article-title: An engineering model of fatigue crack growth under variable amplitude loading publication-title: Int J Fatigue – volume: 85 start-page: 528 year: 1963 end-page: 534 ident: bib129 article-title: A critical analysis of crack propagation laws publication-title: Trans ASME, J Basic Eng Ser D – volume: 46 start-page: 533 year: 2012 end-page: 547 ident: bib88 article-title: Efficient reliability analysis based on Bayesian framework under input variable and metamodel uncertainties publication-title: Struct Multi Optim – volume: 53 start-page: 1 year: 2013 end-page: 8 ident: bib22 article-title: Remaining useful life prediction of grinding mill liners using an artificial neural network publication-title: Miner Eng – volume: 13 start-page: 283 year: 2013 end-page: 291 ident: bib23 article-title: Enhanced fuzzy-filtered neural networks for material fatigue prognosis publication-title: Appl Soft Comput – reference: Wilson AG, Adams RP. Gaussian process covariance kernels for pattern discovery and extrapolation. arXiv preprint arXiv:1302.4245; 2013. URL: – volume: 40 start-page: 273 year: 1998 end-page: 282 ident: bib68 article-title: Prediction intervals for neural networks via nonlinear regression publication-title: Technometrics – volume: 121 start-page: 498 year: 2013 end-page: 511 ident: bib59 article-title: Comparison of a genetic algorithm and simulated annealing for automatic neural network ensemble development publication-title: Neurocomputing – reference: Lawrence, S, Giles, CL, Tsoi, AC., What size neural network gives optimal generalization? Convergence properties of backpropagation technical reports, UM computer science department, UMIACS; Octobor 15, 1998. – volume: 16 start-page: 20 year: 2013 end-page: 25 ident: bib10 article-title: Ball bearing damage detection using traditional signal processing algorithms publication-title: IEEE Instrum Meas Mag – volume: 2 start-page: 163 year: 1999 end-page: 212 ident: bib35 article-title: Survey of neural transfer functions publication-title: Neural Comput Surv – volume: 6 start-page: 1289 year: 1994 end-page: 1301 ident: bib61 article-title: Boosting and other ensemble methods publication-title: Neural Comput – volume: 53 start-page: 805 year: 2013 end-page: 810 ident: bib118 article-title: Remaining useful life prediction of lithium-ion battery with unscented particle filter technique publication-title: Microelectron Reliab – reference: Chen, SC, Lin, SW, Tseng, TY, Lin, HC, Optimization of back-propagation network using simulated annealing approach. In: IEEE international conference on systems, man, and cybernetics, Taipei, Taiwan; October 8–11, 2006. – volume: 11 start-page: 293 year: 2012 end-page: 303 ident: bib122 article-title: Identification of correlated damage parameters under noise and bias using Bayesian inference publication-title: Struct Health Monit – volume: 121 start-page: 20 year: 2014 end-page: 27 ident: bib54 article-title: A new neural network model for the state-of-charge estimation in the battery degradation process publication-title: Appl Energy – volume: 63 start-page: 191 year: 2014 end-page: 207 ident: bib18 article-title: Review of hybrid prognostics approaches for remaining useful life prediction of engineered systems, and an application to battery life prediction publication-title: IEEE Trans Reliab – volume: 34 start-page: 695 year: 2004 end-page: 702 ident: bib46 article-title: A hybrid neural network/genetic algorithm approach to optimizing feature extraction for signal classification publication-title: IEEE Trans Syst Man Cybern Part B Cybern – reference: ; 1997. – volume: 22 start-page: 1341 year: 2011 end-page: 1356 ident: bib78 article-title: Comprehensive review of neural network-based prediction intervals and new advances publication-title: IEEE Tran Neural Networks – year: 2003 ident: bib80 article-title: The design and analysis of computer experiments – volume: 3 start-page: 261 year: 1994 end-page: 276 ident: bib121 article-title: On Markov Chain Monte Carlo acceleration publication-title: J Comput Graph Stat – reference: Bicciato, S, Pandin, M, Didonè, G, Bello, CD., Analysis of an associative memory neural network for pattern identification in gene expression data. In: Biokdd01, workshop on data mining in bioinformatics, pp. 22–30. – volume: 20 start-page: 1483 year: 2006 end-page: 1510 ident: bib1 article-title: A review on machinery diagnostics and prognostics implementing condition-based maintenance publication-title: Mech Syst Sig Process – volume: 50 start-page: 281 year: 2002 end-page: 289 ident: bib132 article-title: Particle filters in state space models with the presence of unknown static parameters publication-title: IEEE Tran Signal Process – volume: 9 start-page: 589 year: 1996 end-page: 601 ident: bib55 article-title: Accelerating backpropagation through dynamic self-adaptation publication-title: Neural Networks – volume: 14 start-page: 69 year: 2004 end-page: 106 ident: bib27 article-title: Gaussian processes for machine learning publication-title: Int J Neural Syst – volume: 50 start-page: 5 year: 2003 end-page: 43 ident: bib115 article-title: An introduction to MCMC for machine learning publication-title: Mach Learn – volume: vol. 6 start-page: 475 year: 1998 end-page: 501 ident: bib89 article-title: Regression and classification using Gaussian process priors publication-title: Bayesian statistics – volume: vol. 1 start-page: 318 year: 1986 end-page: 362 ident: bib38 article-title: Learning internal representations by error propagation publication-title: In: Parallel distributed processing: explorations in the microstructure of cognition – volume: 2013 year: 2013 ident: bib49 article-title: Review on methods to fix number of hidden neurons in neural networks publication-title: Math Prob Eng – volume: 31 start-page: 293 year: 2009 end-page: 308 ident: bib15 article-title: Comparison of prognostic algorithms for estimating remaining useful life of batteries publication-title: Trans Inst Meas Control – volume: 33 start-page: 1065 year: 1962 end-page: 1076 ident: bib74 article-title: On estimation of a probability density function and mode publication-title: Ann Math Stat – volume: 17 start-page: 127 year: 2003 end-page: 140 ident: bib127 article-title: Robust performance degradation assessment methods for enhanced rolling element bearing prognostics publication-title: Adv Eng Inf – reference: Subudhi, B, Jena, D, Gupta, MM, Memetic differential evolution trained neural networks for nonlinear system identification. In: IEEE region 10 colloquium and the third international conference on industrial and information systems, Kharagpur, India; December 8–10, 2008. – volume: 39 start-page: 11128 year: 2014 end-page: 11144 ident: bib25 article-title: Proton exchange membrane fuel cell degradation prediction based on adaptive neuro-fuzzy inference systems publication-title: Int J Hydrogen Energy – reference: Gu, J, Azarian, MH, Pecht, MG., Failure prognostics of multilayer ceramic capacitors in temperature-humidity-bias conditions 2008. In: International conference on prognostics and health management, Denver, Colorado; October 6–9, 2008. – volume: 266 start-page: 162 year: 2013 end-page: 184 ident: bib97 article-title: Virtual model validation of complex multiscale systems: applications to nonlinear elastostatics publication-title: Comput Meth Appl Mech Eng – year: 2006 ident: bib81 article-title: Gaussian processes for machine learning – volume: 4 start-page: 409 year: 1989 end-page: 423 ident: bib87 article-title: Design and analysis of computer experiments publication-title: Stat Sci – reference: Melkumyan, A, Ramos, F., “A sparse covariance function for exact Gaussian process inference in large datasets. In: IJCAI 2009, proceedings of the 21st International joint conference on artificial intelligence, Pasadena, CA., July 11–17; 2009, pp. 1936–1942. – volume: 7 start-page: 985 year: 1994 end-page: 1004 ident: bib44 article-title: The design and evolution of modular neural network architectures publication-title: Neural Networks – volume: 7 start-page: 12 year: 2013 end-page: 24 ident: bib99 article-title: Verification and validation of simulation models publication-title: J Simul – volume: 74 start-page: 111 year: 2012 end-page: 132 ident: bib94 article-title: A full scale approximation of covariance functions for large spatial data sets publication-title: J R Stat Soc Ser B (Stat Methodol) – year: 2002 ident: bib39 article-title: A guide to recurrent neural networks and backpropagation. In: The DALLAS project. Report from the NUTEK-supported project AIS-8: application of data analysis with learning systems, 1999–2001 – reference: Choi JH, An D, Gang J, Joo J, Kim NH. Bayesian approach for parameter estimation in the structural analysis and prognosis. In: Proceedings of the annual conference of the prognostics and health management society, Portland, OR; October 13–16, 2010. – start-page: 1 year: 2013 end-page: 17 ident: bib31 article-title: Residual life estimation based on bivariate non-stationary gamma degradation process publication-title: J Stat Comput Simul – volume: 53 start-page: 370 year: 1763 end-page: 418 ident: bib106 article-title: An essay towards solving a problem in the doctrine of chances publication-title: Philos Trans R Soc London – reference: Freitas, de JFG., “Bayesian methods for neural networks.” PhD thesis. University of Cambridge, UK, 2003. – volume: 196 start-page: 6007 year: 2011 end-page: 6014 ident: bib17 article-title: A review on prognostics and health monitoring of Li-ion battery publication-title: J Power Sources – year: 1994 ident: bib71 article-title: An introduction to the bootstrap – volume: 7 start-page: 221 year: 2007 end-page: 227 ident: bib107 article-title: A particle filtering approach for on-line failure prognosis in a planetary carrier plate publication-title: Int J Fuzzy Logic Intell Syst – volume: 26 start-page: 194 year: 2011 end-page: 201 ident: bib116 article-title: Sequential Monte Carlo filters for abruptly changing state estimation publication-title: Probab Eng Mech – year: 2004 ident: bib79 article-title: Bayesian data analysis – volume: 10 start-page: 857 year: 2009 end-page: 882 ident: bib92 article-title: Stable and efficient Gaussian process calculations publication-title: J Mach Learn Res – volume: 82 start-page: 1032 year: 1987 end-page: 1063 ident: bib112 article-title: Non–Gaussian state space modeling of nonstationary time series (with Discussion), publication-title: J Am Stat Assoc – reference: Guan, X, Liu, Y, Saxena, A, Celaya, J, Goebel, K., Entropy-based probabilistic fatigue damage prognosis and algorithmic performance comparison. In: Annual conference of the prognostics and health management society, San Diego, CA; September 27–October 1, 2009. – volume: 47 start-page: 705 year: 2004 end-page: 712 ident: bib85 article-title: Gaussian process for nonstationary time series prediction publication-title: Comput Stat Data Anal – volume: 19 year: 2013 ident: bib29 article-title: Health assessment and life prediction of cutting tools based on support vector regression publication-title: J Intell Manuf – volume: 7 start-page: 229 year: 1996 end-page: 232 ident: bib67 article-title: Confidence interval prediction for neural network models publication-title: IEEE Tran Neural Networks – reference: MacKay, DJC., Gaussian processes-a replacement for supervised neural networks? Tutorial lecture notes for NIPS, UK, – volume: vol. 7 start-page: 231 year: 1995 end-page: 238 ident: bib62 article-title: Neural network ensembles, cross validation, and active learning publication-title: Advances in neural information processing systems – volume: 95 start-page: 49 year: 2010 end-page: 57 ident: bib24 article-title: A data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear system publication-title: Reliab Eng Syst SafReliab EngSyst Saf – volume: 27 start-page: 613 year: 2013 end-page: 621 ident: bib28 article-title: Wind power grouping forecasts and its uncertainty analysis using optimized relevance vector machine publication-title: Renewable Sustainable Energy Rev – reference: Khawaja, T, Vachtsevanos, G, Wu, B., Reasoning about uncertainty in prognosis: a confidence prediction neural network approach, 2005. In: Annual meeting of the north American Fuzzy Information Processing Society, Ann Arbor, MI; June 22–25, 2005. – volume: 32 start-page: 331 year: 2012 end-page: 348 ident: bib33 article-title: A novel method for online health prognosis of equipment based on hidden semi-Markov model using sequential Monte Carlo methods publication-title: Mech Syst Sig Process – volume: 76 start-page: 167 year: 2002 end-page: 180 ident: bib11 article-title: A condition-based maintenance policy for stochastically deteriorating systems publication-title: Reliab EngSyst Saf – volume: 45 start-page: 422 year: 2013 end-page: 435 ident: bib12 article-title: Condition-based maintenance for continuously monitored degrading systems with multiple failure modes publication-title: Qual Reliab Eng – volume: 34 start-page: 3090 year: 2008 end-page: 3098 ident: bib6 article-title: Fault diagnostics of roller bearing using kernel based neighborhood score multi-class support vector machine publication-title: Expert Syst Appl – volume: 59 start-page: 1 year: 1997 end-page: 23 ident: bib111 article-title: Monte Carlo filter using the genetic algorithm operators publication-title: J Stat Comput Simul – volume: 4 start-page: 46 year: 2008 end-page: 55 ident: bib58 article-title: An improved conjugate gradient based learning algorithm for back propagation neural networks publication-title: Int J Comput Intell – volume: 22 start-page: 428 year: 2011 end-page: 436 ident: bib19 article-title: Health management based on fusion prognostics for avionics systems publication-title: J Syst Eng Electron – volume: 32 start-page: 203 year: 2014 end-page: 212 ident: bib63 article-title: Neural network ensembles based on copula methods and distributed multiobjective central force optimization algorithm publication-title: Eng Appl Artif Intell – reference: Kunli M, Yunxin W. Fault diagnosis of rolling element bearing based on vibration frequency analysis. In: Proceedings of the third international conference on measuring technology and mechatronics automation, Shangshai, China; January 6–7, 2011. – reference: Giurgiutiu V., Current issues in vibration-based fault diagnostics and prognostics. In: SPIE’s ninth annual international symposium on smart structures and materials and seventh annual international symposium on NDE for health monitoring and diagnostics, San Diego, CA; March 17–21, 2002. – volume: 39 start-page: 43 year: 1997 end-page: 62 ident: bib34 article-title: Introduction to multi-layer feed-forward neural networks publication-title: Chemom Intell Lab Syst – volume: 151 start-page: 379 year: 2004 end-page: 384 ident: bib42 article-title: Wavelet neural network approach for fault diagnosis of analogue circuits publication-title: IEEE Proc Circuits Devices Syst – volume: 37 start-page: 241 year: 2012 end-page: 249 ident: bib52 article-title: Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model publication-title: Renewable Energy – volume: 7 start-page: 867 year: 1995 end-page: 888 ident: bib60 article-title: Methods for combining experts’ probability assessments publication-title: Neural Comput – reference: He, W, Williard, N, Osterman, M, Pecht, M., Prognostics of lithium-ion batteries using extended Kalman filtering. In: Proceedings of IMAPS advanced technology workshop on high reliability microelectronics for military applications, Linthicum Heights, MD, USA; May 2011, pp. 17–19. – volume: 13 start-page: 463 year: 2000 end-page: 484 ident: bib69 article-title: Construction of confidence intervals for neural networks based on least squares estimation publication-title: Neural Networks – volume: 58 start-page: 303 year: 2009 end-page: 310 ident: bib126 article-title: Application of blind deconvolution denoising in failure prognosis publication-title: IEEE Tran Instrum Meas – volume: 270 start-page: 828 year: 2011 end-page: 838 ident: bib109 article-title: In-situ monitoring and prediction of progressive joint wear using Bayesian statistics publication-title: Wear – reference: . – volume: 22 start-page: 713 year: 2012 end-page: 722 ident: bib95 article-title: Cases for the nugget in modeling computer experiments publication-title: Stat Comput – volume: 3 start-page: 109 year: 1990 end-page: 118 ident: bib73 article-title: Probabilistic neural networks publication-title: Neural Networks – reference: Neal, RM., Bayesian learning for neural networks PhD thesis, University of Toronto, Ontario, Canada, 1995. – volume: vol. 9 year: 1997 ident: bib82 article-title: Computing with infinite networks publication-title: Advances in neural information processing systems – reference: Wang, WP, Liao, S, Xing, TW., Particle filter for state and parameter estimation in passive ranging. In: IEEE international conference on intelligent computing and intelligent systems. Shanghai, China; 2009. – reference: An, D, Choi, JH, Kim, NH., A comparison study of methods for parameter estimation in the physics-based prognostics. In: 53rd AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics and materials conference, Honolulu, Hawaii; April 23–26, 2012. – reference: Yang, L, Kavli, T, Carlin, M, Clausen, S, Groot, PFM., An evaluation of confidence bound estimation methods for neural networks. In: European symposium on intelligent techniques; 2000, Aachen, Germany, September 14–15, 2000. – reference: Ostafe, D, Neural network hidden layer number determination using pattern recognition techniques. In: Second Romanian-Hungarian joint symposium on applied computational intelligence, Timisoara, Romania; 2005. – volume: 11 start-page: 1009 year: 2000 end-page: 1016 ident: bib76 article-title: Probabilistic neural-network structure determination for pattern classification publication-title: IEEE Tran Neural Networks – reference: Liu, J, Saxena, A, Goebel, K, Saha, B, Wang, W, An adaptive recurrent neural network for remaining useful life prediction of lithium-ion batteries. In: Annual conference of the prognostics and health management society, Portland, Oregon; October 10–16, 2010. – volume: vol. 3 start-page: 395 year: 1998 end-page: 402 ident: bib119 article-title: Using the SIR algorithm to simulate posterior distributions publication-title: Bayesian statistics – volume: 82 start-page: 35 year: 1960 end-page: 45 ident: bib102 article-title: A new approach to linear filtering and prediction problems publication-title: Trans ASME-J Basic Eng – year: 2001 ident: bib104 article-title: Sequential Monte Carlo methods in practice – reference: Xing, Y, Williard, N, Tsui, K-L, Pecht, M., A comparative review of prognostics-based reliability methods for lithium batteries. In: Prognostics and system health management conference, Shenzhen, China; May 24–25, 2011. – volume: 42 start-page: 314 year: 2014 end-page: 334 ident: bib14 article-title: Prognostics and health management design for rotary machinery systems—reviews, methodology and applications publication-title: Mech Syst Sig Process – ident: 10.1016/j.ress.2014.09.014_bib20 – volume: 196 start-page: 6007 year: 2011 ident: 10.1016/j.ress.2014.09.014_bib17 article-title: A review on prognostics and health monitoring of Li-ion battery publication-title: J Power Sources doi: 10.1016/j.jpowsour.2011.03.101 – ident: 10.1016/j.ress.2014.09.014_bib43 – ident: 10.1016/j.ress.2014.09.014_bib105 doi: 10.36001/phmconf.2010.v2i1.1753 – volume: 17 start-page: 127 year: 2003 ident: 10.1016/j.ress.2014.09.014_bib127 article-title: Robust performance degradation assessment methods for enhanced rolling element bearing prognostics publication-title: Adv Eng Inf doi: 10.1016/j.aei.2004.08.001 – volume: 2013 year: 2013 ident: 10.1016/j.ress.2014.09.014_bib49 article-title: Review on methods to fix number of hidden neurons in neural networks publication-title: Math Prob Eng doi: 10.1155/2013/425740 – volume: 7 start-page: 6837 issue: 12 year: 2011 ident: 10.1016/j.ress.2014.09.014_bib51 article-title: Particle swarm optimization based on back propagation network forecasting exchange rates publication-title: Int J Innovative Comput Inf Control – volume: 4 start-page: 409 issue: 4 year: 1989 ident: 10.1016/j.ress.2014.09.014_bib87 article-title: Design and analysis of computer experiments publication-title: Stat Sci doi: 10.1214/ss/1177012413 – ident: 10.1016/j.ress.2014.09.014_bib57 doi: 10.1109/ICIINFS.2008.4798417 – volume: 20 start-page: 1483 issue: 7 year: 2006 ident: 10.1016/j.ress.2014.09.014_bib1 article-title: A review on machinery diagnostics and prognostics implementing condition-based maintenance publication-title: Mech Syst Sig Process doi: 10.1016/j.ymssp.2005.09.012 – volume: 9 start-page: 589 issue: 4 year: 1996 ident: 10.1016/j.ress.2014.09.014_bib55 article-title: Accelerating backpropagation through dynamic self-adaptation publication-title: Neural Networks doi: 10.1016/0893-6080(95)00144-1 – volume: 40 start-page: 273 issue: 4 year: 1998 ident: 10.1016/j.ress.2014.09.014_bib68 article-title: Prediction intervals for neural networks via nonlinear regression publication-title: Technometrics doi: 10.2307/1270528 – volume: 62 start-page: 1 year: 2014 ident: 10.1016/j.ress.2014.09.014_bib4 article-title: Fault diagnosis for a wind turbine transmission system based on manifold learning and Shannon wavelet support vector machine publication-title: Renewable Energy doi: 10.1016/j.renene.2013.06.025 – ident: 10.1016/j.ress.2014.09.014_bib131 doi: 10.2514/6.2012-1435 – volume: 270 start-page: 828 issue: 11–12 year: 2011 ident: 10.1016/j.ress.2014.09.014_bib109 article-title: In-situ monitoring and prediction of progressive joint wear using Bayesian statistics publication-title: Wear doi: 10.1016/j.wear.2011.02.010 – volume: 74 start-page: 111 issue: 1 year: 2012 ident: 10.1016/j.ress.2014.09.014_bib94 article-title: A full scale approximation of covariance functions for large spatial data sets publication-title: J R Stat Soc Ser B (Stat Methodol) doi: 10.1111/j.1467-9868.2011.01007.x – ident: 10.1016/j.ress.2014.09.014_bib66 – volume: 49 start-page: 1965 issue: 6 year: 2012 ident: 10.1016/j.ress.2014.09.014_bib101 article-title: Using a simple crack growth model in predicting remaining useful life publication-title: J Aircr doi: 10.2514/1.C031808 – ident: 10.1016/j.ress.2014.09.014_bib117 – volume: 34 start-page: 3090 year: 2008 ident: 10.1016/j.ress.2014.09.014_bib6 article-title: Fault diagnostics of roller bearing using kernel based neighborhood score multi-class support vector machine publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2007.06.029 – volume: 53 start-page: 370 year: 1763 ident: 10.1016/j.ress.2014.09.014_bib106 article-title: An essay towards solving a problem in the doctrine of chances publication-title: Philos Trans R Soc London doi: 10.1098/rstl.1763.0053 – volume: 30 start-page: 2 issue: 1 year: 2008 ident: 10.1016/j.ress.2014.09.014_bib130 article-title: An engineering model of fatigue crack growth under variable amplitude loading publication-title: Int J Fatigue doi: 10.1016/j.ijfatigue.2007.03.004 – volume: 30 start-page: 71 year: 2009 ident: 10.1016/j.ress.2014.09.014_bib48 article-title: Neural network architecture selection: can function complexity help? publication-title: Neural Proc Lett doi: 10.1007/s11063-009-9108-2 – volume: 31 start-page: 293 issue: 3–4 year: 2009 ident: 10.1016/j.ress.2014.09.014_bib15 article-title: Comparison of prognostic algorithms for estimating remaining useful life of batteries publication-title: Trans Inst Meas Control doi: 10.1177/0142331208092030 – ident: 10.1016/j.ress.2014.09.014_bib103 – ident: 10.1016/j.ress.2014.09.014_bib123 doi: 10.1109/PHM.2008.4711464 – volume: 58 start-page: 303 issue: 2 year: 2009 ident: 10.1016/j.ress.2014.09.014_bib126 article-title: Application of blind deconvolution denoising in failure prognosis publication-title: IEEE Tran Instrum Meas doi: 10.1109/TIM.2008.2005963 – year: 2004 ident: 10.1016/j.ress.2014.09.014_bib41 – ident: 10.1016/j.ress.2014.09.014_bib124 – ident: 10.1016/j.ress.2014.09.014_bib86 – volume: 2 start-page: 1771 issue: 6 year: 2013 ident: 10.1016/j.ress.2014.09.014_bib120 article-title: Improved MCMC method for parameter estimation based on marginal probability density function publication-title: J Mech Sci Technol doi: 10.1007/s12206-013-0428-9 – ident: 10.1016/j.ress.2014.09.014_bib56 doi: 10.1109/ICSMC.2006.385301 – volume: 13 start-page: 283 issue: 1 year: 2013 ident: 10.1016/j.ress.2014.09.014_bib23 article-title: Enhanced fuzzy-filtered neural networks for material fatigue prognosis publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2012.08.031 – ident: 10.1016/j.ress.2014.09.014_bib113 doi: 10.1109/ICICISYS.2009.5358175 – volume: 46 start-page: 533 year: 2012 ident: 10.1016/j.ress.2014.09.014_bib88 article-title: Efficient reliability analysis based on Bayesian framework under input variable and metamodel uncertainties publication-title: Struct Multi Optim doi: 10.1007/s00158-012-0776-6 – ident: 10.1016/j.ress.2014.09.014_bib93 – ident: 10.1016/j.ress.2014.09.014_bib70 – volume: 17 start-page: 441 year: 2013 ident: 10.1016/j.ress.2014.09.014_bib45 article-title: Toward a hybrid approach of primitive cognitive network process and particle swarm optimization neural network for forecasting publication-title: Procedia Comput Sci doi: 10.1016/j.procs.2013.05.057 – volume: 33 start-page: 1065 issue: 3 year: 1962 ident: 10.1016/j.ress.2014.09.014_bib74 article-title: On estimation of a probability density function and mode publication-title: Ann Math Stat doi: 10.1214/aoms/1177704472 – volume: 11 start-page: 1009 issue: 4 year: 2000 ident: 10.1016/j.ress.2014.09.014_bib76 article-title: Probabilistic neural-network structure determination for pattern classification publication-title: IEEE Tran Neural Networks doi: 10.1109/72.857781 – volume: 50 start-page: 281 issue: 2 year: 2002 ident: 10.1016/j.ress.2014.09.014_bib132 article-title: Particle filters in state space models with the presence of unknown static parameters publication-title: IEEE Tran Signal Process doi: 10.1109/78.978383 – volume: 16 start-page: 20 issue: 2 year: 2013 ident: 10.1016/j.ress.2014.09.014_bib10 article-title: Ball bearing damage detection using traditional signal processing algorithms publication-title: IEEE Instrum Meas Mag doi: 10.1109/MIM.2013.6495676 – volume: 213 start-page: 1 year: 2011 ident: 10.1016/j.ress.2014.09.014_bib13 article-title: Remaining useful life estimation—a review on the statistical data driven approaches publication-title: Eur J Oper Res doi: 10.1016/j.ejor.2010.11.018 – ident: 10.1016/j.ress.2014.09.014_bib16 – year: 2004 ident: 10.1016/j.ress.2014.09.014_bib79 – volume: 4 start-page: 46 issue: 1 year: 2008 ident: 10.1016/j.ress.2014.09.014_bib58 article-title: An improved conjugate gradient based learning algorithm for back propagation neural networks publication-title: Int J Comput Intell – volume: 19 year: 2013 ident: 10.1016/j.ress.2014.09.014_bib29 article-title: Health assessment and life prediction of cutting tools based on support vector regression publication-title: J Intell Manuf – volume: 82 start-page: 1032 issue: 400 year: 1987 ident: 10.1016/j.ress.2014.09.014_bib112 article-title: Non–Gaussian state space modeling of nonstationary time series (with Discussion), publication-title: J Am Stat Assoc – start-page: 1 year: 2013 ident: 10.1016/j.ress.2014.09.014_bib31 article-title: Residual life estimation based on bivariate non-stationary gamma degradation process publication-title: J Stat Comput Simul doi: 10.1080/00949655.2013.848452 – ident: 10.1016/j.ress.2014.09.014_bib75 doi: 10.1117/12.469869 – volume: 82 start-page: 35 year: 1960 ident: 10.1016/j.ress.2014.09.014_bib102 article-title: A new approach to linear filtering and prediction problems publication-title: Trans ASME-J Basic Eng doi: 10.1115/1.3662552 – volume: 7 start-page: 221 issue: 4 year: 2007 ident: 10.1016/j.ress.2014.09.014_bib107 article-title: A particle filtering approach for on-line failure prognosis in a planetary carrier plate publication-title: Int J Fuzzy Logic Intell Syst doi: 10.5391/IJFIS.2007.7.4.221 – ident: 10.1016/j.ress.2014.09.014_bib108 doi: 10.1109/AERO.2011.5747574 – volume: 26 start-page: 194 year: 2011 ident: 10.1016/j.ress.2014.09.014_bib116 article-title: Sequential Monte Carlo filters for abruptly changing state estimation publication-title: Probab Eng Mech doi: 10.1016/j.probengmech.2010.07.010 – year: 1994 ident: 10.1016/j.ress.2014.09.014_bib71 – volume: 39 start-page: 11128 issue: 21 year: 2014 ident: 10.1016/j.ress.2014.09.014_bib25 article-title: Proton exchange membrane fuel cell degradation prediction based on adaptive neuro-fuzzy inference systems publication-title: Int J Hydrogen Energy doi: 10.1016/j.ijhydene.2014.05.005 – volume: 2 start-page: 163 year: 1999 ident: 10.1016/j.ress.2014.09.014_bib35 article-title: Survey of neural transfer functions publication-title: Neural Comput Surv – volume: 63 start-page: 191 issue: 1 year: 2014 ident: 10.1016/j.ress.2014.09.014_bib18 article-title: Review of hybrid prognostics approaches for remaining useful life prediction of engineered systems, and an application to battery life prediction publication-title: IEEE Trans Reliab doi: 10.1109/TR.2014.2299152 – volume: 20 start-page: 887 year: 2009 ident: 10.1016/j.ress.2014.09.014_bib83 article-title: Gaussian process time series model for life prognosis of metallic structures publication-title: J Intell Mater Syst Struct doi: 10.1177/1045389X08099602 – volume: 59 start-page: 1 issue: 1 year: 1997 ident: 10.1016/j.ress.2014.09.014_bib111 article-title: Monte Carlo filter using the genetic algorithm operators publication-title: J Stat Comput Simul doi: 10.1080/00949659708811843 – volume: 85 start-page: 528 issue: 3 year: 1963 ident: 10.1016/j.ress.2014.09.014_bib129 article-title: A critical analysis of crack propagation laws publication-title: Trans ASME, J Basic Eng Ser D doi: 10.1115/1.3656900 – volume: 6 start-page: 1289 issue: 6 year: 1994 ident: 10.1016/j.ress.2014.09.014_bib61 article-title: Boosting and other ensemble methods publication-title: Neural Comput doi: 10.1162/neco.1994.6.6.1289 – volume: 7 start-page: 229 issue: 1 year: 1996 ident: 10.1016/j.ress.2014.09.014_bib67 article-title: Confidence interval prediction for neural network models publication-title: IEEE Tran Neural Networks doi: 10.1109/72.478409 – volume: 7 start-page: 985 year: 1994 ident: 10.1016/j.ress.2014.09.014_bib44 article-title: The design and evolution of modular neural network architectures publication-title: Neural Networks doi: 10.1016/S0893-6080(05)80155-8 – ident: 10.1016/j.ress.2014.09.014_bib90 – volume: 35 start-page: 219 issue: 1–2 year: 2013 ident: 10.1016/j.ress.2014.09.014_bib32 article-title: A wiener process-based degradation model with a recursive filter algorithm for remaining useful life estimation publication-title: Mech Syst Sig Process doi: 10.1016/j.ymssp.2012.08.016 – volume: 151 start-page: 379 issue: 4 year: 2004 ident: 10.1016/j.ress.2014.09.014_bib42 article-title: Wavelet neural network approach for fault diagnosis of analogue circuits publication-title: IEEE Proc Circuits Devices Syst doi: 10.1049/ip-cds:20040495 – volume: 3 start-page: 261 year: 1994 ident: 10.1016/j.ress.2014.09.014_bib121 article-title: On Markov Chain Monte Carlo acceleration publication-title: J Comput Graph Stat doi: 10.1080/10618600.1994.10474644 – volume: 53 start-page: 1 year: 2013 ident: 10.1016/j.ress.2014.09.014_bib22 article-title: Remaining useful life prediction of grinding mill liners using an artificial neural network publication-title: Miner Eng doi: 10.1016/j.mineng.2013.05.026 – volume: vol. 1 start-page: 318 year: 1986 ident: 10.1016/j.ress.2014.09.014_bib38 article-title: Learning internal representations by error propagation – volume: 63 start-page: 127 issue: Part 1 year: 2001 ident: 10.1016/j.ress.2014.09.014_bib114 article-title: Following a moving target-Monte Carlo inference for dynamic Bayesian models publication-title: R Stat Soc B doi: 10.1111/1467-9868.00280 – volume: 39 start-page: 43 year: 1997 ident: 10.1016/j.ress.2014.09.014_bib34 article-title: Introduction to multi-layer feed-forward neural networks publication-title: Chemom Intell Lab Syst doi: 10.1016/S0169-7439(97)00061-0 – volume: 21 start-page: 824 year: 2007 ident: 10.1016/j.ress.2014.09.014_bib7 article-title: Approximate entropy as a diagnostic tool for machine health monitoring publication-title: Mech Syst Sig Process doi: 10.1016/j.ymssp.2006.02.009 – volume: 22 start-page: 713 issue: 3 year: 2012 ident: 10.1016/j.ress.2014.09.014_bib95 article-title: Cases for the nugget in modeling computer experiments publication-title: Stat Comput doi: 10.1007/s11222-010-9224-x – volume: 115 start-page: 161 year: 2013 ident: 10.1016/j.ress.2014.09.014_bib110 article-title: Prognostics 101: a tutorial for particle filter-based prognostics algorithm using Matlab publication-title: Reliab EngSyst Saf doi: 10.1016/j.ress.2013.02.019 – volume: vol. 16 year: 2004 ident: 10.1016/j.ress.2014.09.014_bib84 article-title: Nonstationary covariance functions for Gaussian process regression – volume: 9 start-page: 15 issue: 1 year: 2013 ident: 10.1016/j.ress.2014.09.014_bib53 article-title: Developing an artificial neural network model to evaluate chloride diffusivity in high performance concrete publication-title: HBRC J doi: 10.1016/j.hbrcj.2013.04.001 – volume: 76 start-page: 167 issue: 2 year: 2002 ident: 10.1016/j.ress.2014.09.014_bib11 article-title: A condition-based maintenance policy for stochastically deteriorating systems publication-title: Reliab EngSyst Saf doi: 10.1016/S0951-8320(01)00148-X – volume: 5 start-page: 961 year: 1992 ident: 10.1016/j.ress.2014.09.014_bib21 article-title: Forecasting the behavior of multivariate time series using neural networks publication-title: Neural Networks doi: 10.1016/S0893-6080(05)80092-9 – volume: 56 start-page: 4215 year: 2012 ident: 10.1016/j.ress.2014.09.014_bib96 article-title: The effect of the nugget on Gaussian process emulators of computer models publication-title: Comput Stat Data Anal doi: 10.1016/j.csda.2012.04.020 – volume: 32 start-page: 331 year: 2012 ident: 10.1016/j.ress.2014.09.014_bib33 article-title: A novel method for online health prognosis of equipment based on hidden semi-Markov model using sequential Monte Carlo methods publication-title: Mech Syst Sig Process doi: 10.1016/j.ymssp.2012.05.004 – ident: 10.1016/j.ress.2014.09.014_bib40 doi: 10.1109/IJCNN.2001.938431 – volume: 3 start-page: 109 year: 1990 ident: 10.1016/j.ress.2014.09.014_bib73 article-title: Probabilistic neural networks publication-title: Neural Networks doi: 10.1016/0893-6080(90)90049-Q – ident: 10.1016/j.ress.2014.09.014_bib77 doi: 10.1109/NAFIPS.2005.1548498 – ident: 10.1016/j.ress.2014.09.014_bib125 – volume: 282 start-page: 475 issue: 1–2 year: 2005 ident: 10.1016/j.ress.2014.09.014_bib3 article-title: A review of vibration-based techniques for helicopter transmission diagnostics publication-title: J Sound Vib doi: 10.1016/j.jsv.2004.02.058 – ident: 10.1016/j.ress.2014.09.014_bib47 – volume: 34 start-page: 695 issue: 1 year: 2004 ident: 10.1016/j.ress.2014.09.014_bib46 article-title: A hybrid neural network/genetic algorithm approach to optimizing feature extraction for signal classification publication-title: IEEE Trans Syst Man Cybern Part B Cybern doi: 10.1109/TSMCB.2003.811293 – volume: 339 start-page: 1195 year: 2003 ident: 10.1016/j.ress.2014.09.014_bib37 article-title: Estimating photometric redshifts with artificial neural networks publication-title: Mon Not R Astron Soc doi: 10.1046/j.1365-8711.2003.06271.x – year: 2002 ident: 10.1016/j.ress.2014.09.014_bib39 article-title: A guide to recurrent neural networks and backpropagation. In: The DALLAS project. Report from the NUTEK-supported project AIS-8: application of data analysis with learning systems, 1999–2001 – ident: 10.1016/j.ress.2014.09.014_bib50 – volume: 121 start-page: 20 year: 2014 ident: 10.1016/j.ress.2014.09.014_bib54 article-title: A new neural network model for the state-of-charge estimation in the battery degradation process publication-title: Appl Energy doi: 10.1016/j.apenergy.2014.01.066 – volume: 27 start-page: 613 year: 2013 ident: 10.1016/j.ress.2014.09.014_bib28 article-title: Wind power grouping forecasts and its uncertainty analysis using optimized relevance vector machine publication-title: Renewable Sustainable Energy Rev doi: 10.1016/j.rser.2013.07.026 – volume: 32 start-page: 203 year: 2014 ident: 10.1016/j.ress.2014.09.014_bib63 article-title: Neural network ensembles based on copula methods and distributed multiobjective central force optimization algorithm publication-title: Eng Appl Artif Intell doi: 10.1016/j.engappai.2014.02.009 – volume: 22 start-page: 428 issue: 3 year: 2011 ident: 10.1016/j.ress.2014.09.014_bib19 article-title: Health management based on fusion prognostics for avionics systems publication-title: J Syst Eng Electron doi: 10.3969/j.issn.1004-4132.2011.03.010 – volume: vol. 15 start-page: 625 year: 2003 ident: 10.1016/j.ress.2014.09.014_bib91 article-title: Fast sparse Gaussian process methods: the information vector machine – volume: 266 start-page: 162 year: 2013 ident: 10.1016/j.ress.2014.09.014_bib97 article-title: Virtual model validation of complex multiscale systems: applications to nonlinear elastostatics publication-title: Comput Meth Appl Mech Eng doi: 10.1016/j.cma.2013.07.011 – volume: vol. 7 start-page: 231 year: 1995 ident: 10.1016/j.ress.2014.09.014_bib62 article-title: Neural network ensembles, cross validation, and active learning – volume: vol. 6 start-page: 475 year: 1998 ident: 10.1016/j.ress.2014.09.014_bib89 article-title: Regression and classification using Gaussian process priors – volume: 49 start-page: 2818 issue: 12 year: 2011 ident: 10.1016/j.ress.2014.09.014_bib30 article-title: Uncertainty identification of damage growth parameters using nonlinear regression publication-title: AIAA J doi: 10.2514/1.J051268 – ident: 10.1016/j.ress.2014.09.014_bib65 – volume: 7 start-page: 867 issue: 5 year: 1995 ident: 10.1016/j.ress.2014.09.014_bib60 article-title: Methods for combining experts’ probability assessments publication-title: Neural Comput doi: 10.1162/neco.1995.7.5.867 – volume: 45 start-page: 422 issue: 4 year: 2013 ident: 10.1016/j.ress.2014.09.014_bib12 article-title: Condition-based maintenance for continuously monitored degrading systems with multiple failure modes publication-title: Qual Reliab Eng – volume: vol. 9 year: 1997 ident: 10.1016/j.ress.2014.09.014_bib82 article-title: Computing with infinite networks – volume: 11 start-page: 293 issue: 3 year: 2012 ident: 10.1016/j.ress.2014.09.014_bib122 article-title: Identification of correlated damage parameters under noise and bias using Bayesian inference publication-title: Struct Health Monit doi: 10.1177/1475921711424520 – volume: 42 start-page: 314 issue: 1–2 year: 2014 ident: 10.1016/j.ress.2014.09.014_bib14 article-title: Prognostics and health management design for rotary machinery systems—reviews, methodology and applications publication-title: Mech Syst Sig Process doi: 10.1016/j.ymssp.2013.06.004 – volume: 37 start-page: 241 issue: 1 year: 2012 ident: 10.1016/j.ress.2014.09.014_bib52 article-title: Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model publication-title: Renewable Energy doi: 10.1016/j.renene.2011.06.023 – volume: 95 start-page: 49 year: 2010 ident: 10.1016/j.ress.2014.09.014_bib24 article-title: A data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear system publication-title: Reliab Eng Syst SafReliab EngSyst Saf doi: 10.1016/j.ress.2009.08.001 – volume: 14 start-page: 69 issue: 2 year: 2004 ident: 10.1016/j.ress.2014.09.014_bib27 article-title: Gaussian processes for machine learning publication-title: Int J Neural Syst doi: 10.1142/S0129065704001899 – year: 2001 ident: 10.1016/j.ress.2014.09.014_bib104 – volume: 1 start-page: 81 issue: 1 year: 2011 ident: 10.1016/j.ress.2014.09.014_bib128 article-title: Robust prognostics concept for gearbox with artificially induced gear crack utilizing acoustic emission publication-title: Energy Environ Res doi: 10.5539/eer.v1n1p81 – volume: 25 start-page: 2169 year: 2011 ident: 10.1016/j.ress.2014.09.014_bib9 article-title: Improvement of damage detection methods based on experimental modal parameters publication-title: Mech Syst Sig Process doi: 10.1016/j.ymssp.2011.01.007 – volume: 91 start-page: 1390 year: 2006 ident: 10.1016/j.ress.2014.09.014_bib98 article-title: Validation and error estimation of computational models publication-title: Reliab Eng Syst Saf doi: 10.1016/j.ress.2005.11.035 – volume: 111 start-page: 217 year: 2013 ident: 10.1016/j.ress.2014.09.014_bib100 article-title: Quantitative model validation techniques: new insights publication-title: Reliab EngSyst Saf doi: 10.1016/j.ress.2012.11.011 – volume: 10 start-page: 857 year: 2009 ident: 10.1016/j.ress.2014.09.014_bib92 article-title: Stable and efficient Gaussian process calculations publication-title: J Mach Learn Res – volume: 34 start-page: 527 issue: 4 year: 1994 ident: 10.1016/j.ress.2014.09.014_bib2 article-title: A review by discussion of condition monitoring and fault diagnosis in machine tools publication-title: Int J Mach Tools Manuf doi: 10.1016/0890-6955(94)90083-3 – volume: 13 start-page: 463 issue: 4–5 year: 2000 ident: 10.1016/j.ress.2014.09.014_bib69 article-title: Construction of confidence intervals for neural networks based on least squares estimation publication-title: Neural Networks doi: 10.1016/S0893-6080(99)00080-5 – volume: 50 start-page: 5 issue: 1 year: 2003 ident: 10.1016/j.ress.2014.09.014_bib115 article-title: An introduction to MCMC for machine learning publication-title: Mach Learn doi: 10.1023/A:1020281327116 – ident: 10.1016/j.ress.2014.09.014_bib26 – volume: vol. 3 start-page: 395 year: 1998 ident: 10.1016/j.ress.2014.09.014_bib119 article-title: Using the SIR algorithm to simulate posterior distributions – volume: 22 start-page: 1341 issue: 9 year: 2011 ident: 10.1016/j.ress.2014.09.014_bib78 article-title: Comprehensive review of neural network-based prediction intervals and new advances publication-title: IEEE Tran Neural Networks doi: 10.1109/TNN.2011.2162110 – year: 2006 ident: 10.1016/j.ress.2014.09.014_bib81 – year: 2003 ident: 10.1016/j.ress.2014.09.014_bib80 – volume: 6 year: 2014 ident: 10.1016/j.ress.2014.09.014_bib5 article-title: Applications of laser diagnostics to thermal power plants and engines publication-title: Appl Therm Eng – ident: 10.1016/j.ress.2014.09.014_bib8 doi: 10.1109/ICMTMA.2011.337 – volume: 121 start-page: 498 year: 2013 ident: 10.1016/j.ress.2014.09.014_bib59 article-title: Comparison of a genetic algorithm and simulated annealing for automatic neural network ensemble development publication-title: Neurocomputing doi: 10.1016/j.neucom.2013.05.024 – volume: 112 start-page: 120 year: 2013 ident: 10.1016/j.ress.2014.09.014_bib72 article-title: Quantifying uncertainties of neural network-based electricity price forecasts publication-title: Appl Energy doi: 10.1016/j.apenergy.2013.05.075 – ident: 10.1016/j.ress.2014.09.014_bib36 doi: 10.36001/phmconf.2010.v2i1.1896 – volume: 8 start-page: 283 issue: 3 year: 1997 ident: 10.1016/j.ress.2014.09.014_bib64 article-title: Optimal ensemble averaging of neural networks publication-title: Network Comput Neural Syst doi: 10.1088/0954-898X/8/3/004 – volume: 7 start-page: 12 year: 2013 ident: 10.1016/j.ress.2014.09.014_bib99 article-title: Verification and validation of simulation models publication-title: J Simul doi: 10.1057/jos.2012.20 – volume: 47 start-page: 705 year: 2004 ident: 10.1016/j.ress.2014.09.014_bib85 article-title: Gaussian process for nonstationary time series prediction publication-title: Comput Stat Data Anal doi: 10.1016/j.csda.2004.02.006 – volume: 53 start-page: 805 year: 2013 ident: 10.1016/j.ress.2014.09.014_bib118 article-title: Remaining useful life prediction of lithium-ion battery with unscented particle filter technique publication-title: Microelectron Reliab doi: 10.1016/j.microrel.2012.12.004 |
| SSID | ssj0004957 |
| Score | 2.5874784 |
| SecondaryResourceType | review_article |
| Snippet | This paper is to provide practical options for prognostics so that beginners can select appropriate methods for their fields of application. To achieve this... |
| SourceID | proquest pascalfrancis crossref elsevier |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 223 |
| SubjectTerms | Algorithms Applied sciences Bayesian inference Bias Complexity Data-driven prognostics Exact sciences and technology Fatigue failure Fracture mechanics (crack, fatigue, damage...) Fundamental areas of phenomenology (including applications) Gaussian process regression Mathematics Neural network Neural networks Noise Operational research and scientific management Operational research. Management science Parametric inference Particle filter Physics Physics-based prognostics Probability and statistics Reliability engineering Reliability theory. Replacement problems Sampling theory, sample surveys Sciences and techniques of general use Solid mechanics Statistics Structural and continuum mechanics |
| Title | Practical options for selecting data-driven or physics-based prognostics algorithms with reviews |
| URI | https://dx.doi.org/10.1016/j.ress.2014.09.014 https://www.proquest.com/docview/1651454583 |
| Volume | 133 |
| WOSCitedRecordID | wos000345469600022&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1879-0836 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0004957 issn: 0951-8320 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1bb9MwFLagA2kIcSkgymUyEm-VUZyksf1YTUMtaGUPQ_QtuI4zmLakajo0_j3HOU6abmLAAy9p6zRJ5fP13HzOZ0Lecp0AkDLBwNYbFicjwbQZxUzaOJM6VoLzRb3ZhJjN5HyujvyKaVVvJyCKQl5equV_FTWMgbBd6-w_iLu9KQzAexA6HEHscPwrwSMDkZv6EgtW6krCqt7vxuUFXE0oy1ZOy7lidExtVMyZs6wu1ypKz918dlKuvq-_nfsOOE9c2nVnXUEzEn3_HNoNs2GNJ-SIHlY691Qjtb88_jIbjtvln4_TQ1Tx55smif3Jpynm9Us2KbtZCezIxCSZt-hbmUbOQHEEW0o3irpqE3uOvQUOkRLlmnLHPMPpO5eHcEV5yFCLPajbTNpXLFxbdxhCvC_CRN4mO6GAUKpHdsbTg_mHTSetQm7Y5if7NiusCLz63N-5MveXugIp57gzyjUjX3sux4_IAx9y0DFC5TG5ZYs-eejDD-qVe9Un9zrclPDpsCX0hXN3jxAmfbLrwhNk935CvrZgox5sFMBGW7DRDtgonNgCG-2AjW7ARh3YqAfbU_L5_cHx_oT5PTuYiZJozWwsuYGoTYtELbjNMh0JJW0mRbQwgcq5Af-Zg9uUGc0VDJsY5nkU2kwvsjDLo2ekV5SFfU6oCWQ-khHPQyVjG1stQxNYLoyyUss4HxDeTH9qPKG921flLG0qF09TJ7LUiSwNVAovAzJsr1kincuN3x41Uk29Q4qOZgqQvPG6vS0ItI9q8DcgbxpMpKDO3RqdLmx5UaU8gQjGLWZHL_50k5dk1_3zMB_4ivTWqwv7mtwxPwAEqz2P7F_CVcPN |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Practical+options+for+selecting+data-driven+or+physics-based+prognostics+algorithms+with+reviews&rft.jtitle=Reliability+engineering+%26+system+safety&rft.au=DAWN+AN&rft.au=KIM%2C+Nam+H&rft.au=CHOI%2C+Joo-Ho&rft.date=2015&rft.pub=Elsevier&rft.issn=0951-8320&rft.volume=133&rft.spage=223&rft.epage=236&rft_id=info:doi/10.1016%2Fj.ress.2014.09.014&rft.externalDBID=n%2Fa&rft.externalDocID=28887268 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0951-8320&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0951-8320&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0951-8320&client=summon |