Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice
•Main PHM challenges in industry 4.0: physics, data and solution requirements.•Data challenges: missing of anomalies, labels and the continuously monitored data.•Advancing methods of detection, diagnostics and prognostics for data challenge.•Solution requirements challenge: interpretability, securit...
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| Vydáno v: | Reliability engineering & system safety Ročník 218; s. 108119 |
|---|---|
| Hlavní autor: | |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Barking
Elsevier Ltd
01.02.2022
Elsevier BV Elsevier |
| Témata: | |
| ISSN: | 0951-8320, 1879-0836 |
| On-line přístup: | Získat plný text |
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| Abstract | •Main PHM challenges in industry 4.0: physics, data and solution requirements.•Data challenges: missing of anomalies, labels and the continuously monitored data.•Advancing methods of detection, diagnostics and prognostics for data challenge.•Solution requirements challenge: interpretability, security, uncertainty of models.
We are performing the digital transition of industry, living the 4th industrial revolution, building a new World in which the digital, physical and human dimensions are interrelated in complex socio-cyber-physical systems. For the sustainability of these transformations, knowledge, information and data must be integrated within model-based and data-driven approaches of Prognostics and Health Management (PHM) for the assessment and prediction of structures, systems and components (SSCs) evolutions and process behaviors, so as to allow anticipating failures and avoiding accidents, thus, aiming at improved safe and reliable design, operation and maintenance.
There is already a plethora of methods available for many potential applications and more are being developed: yet, there are still a number of critical problems which impede full deployment of PHM and its benefits in practice. In this respect, this paper does not aim at providing a survey of existing works for an introduction to PHM nor at providing new tools or methods for its further development; rather, it aims at pointing out main challenges and directions of advancements, for full deployment of condition-based and predictive maintenance in practice. |
|---|---|
| AbstractList | •Main PHM challenges in industry 4.0: physics, data and solution requirements.•Data challenges: missing of anomalies, labels and the continuously monitored data.•Advancing methods of detection, diagnostics and prognostics for data challenge.•Solution requirements challenge: interpretability, security, uncertainty of models.
We are performing the digital transition of industry, living the 4th industrial revolution, building a new World in which the digital, physical and human dimensions are interrelated in complex socio-cyber-physical systems. For the sustainability of these transformations, knowledge, information and data must be integrated within model-based and data-driven approaches of Prognostics and Health Management (PHM) for the assessment and prediction of structures, systems and components (SSCs) evolutions and process behaviors, so as to allow anticipating failures and avoiding accidents, thus, aiming at improved safe and reliable design, operation and maintenance.
There is already a plethora of methods available for many potential applications and more are being developed: yet, there are still a number of critical problems which impede full deployment of PHM and its benefits in practice. In this respect, this paper does not aim at providing a survey of existing works for an introduction to PHM nor at providing new tools or methods for its further development; rather, it aims at pointing out main challenges and directions of advancements, for full deployment of condition-based and predictive maintenance in practice. We are performing the digital transition of industry, living the 4th industrial revolution, building a new World in which the digital, physical and human dimensions are interrelated in complex socio-cyber-physical systems. For the sustainability of these transformations, knowledge, information and data must be integrated within model-based and data-driven approaches of Prognostics and Health Management (PHM) for the assessment and prediction of structures, systems and components (SSCs) evolutions and process behaviors, so as to allow anticipating failures and avoiding accidents, thus, aiming at improved safe and reliable design, operation and maintenance.There is already a plethora of methods available for many potential applications and more are being developed: yet, there are still a number of critical problems which impede full deployment of PHM and its benefits in practice. In this respect, this paper does not aim at providing a survey of existing works for an introduction to PHM nor at providing new tools or methods for its further development; rather, it aims at pointing out main challenges and directions of advancements, for full deployment of condition-based and predictive maintenance in practice. |
| ArticleNumber | 108119 |
| Author | Zio, Enrico |
| Author_xml | – sequence: 1 givenname: Enrico surname: Zio fullname: Zio, Enrico email: enrico.zio@mines-paristech.fr, enrico.zio@polimi.it organization: MINES ParisTech, PSL Research University, CRC, Sophia Antipolis, France |
| BackLink | https://minesparis-psl.hal.science/hal-03907690$$DView record in HAL |
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| Cites_doi | 10.1016/j.jcp.2018.10.045 10.1016/j.ress.2014.03.014 10.1007/s40747-016-0019-3 10.1016/j.asoc.2010.08.011 10.1016/j.jsp.2009.10.001 10.1016/j.ymssp.2014.12.018 10.1016/j.ejor.2015.06.032 10.1016/j.ymssp.2019.01.060 10.1007/s00449-006-0109-5 10.1109/TPAMI.2018.2799847 10.1016/j.ress.2019.03.018 10.1007/s10994-010-5188-5 10.1016/j.ress.2019.02.015 10.1016/j.ress.2019.03.044 10.1109/ACCESS.2016.2587754 10.1109/TNNLS.2018.2828699 10.1016/j.ress.2010.06.001 10.1097/EDE.0b013e3182576cdb 10.1002/we.2102 10.1016/j.ress.2018.11.011 10.1214/aos/1013203451 10.1016/j.ress.2019.106676 10.1016/j.ymssp.2014.09.013 10.1109/TIE.2016.2519325 10.1016/j.mfglet.2018.02.003 10.1177/1475921710388972 10.1007/s41872-020-00119-y 10.1016/j.apor.2017.12.009 10.1109/TIE.2019.2907440 10.1109/MSP.2017.2695801 10.1016/j.ress.2018.11.027 10.1109/TPAMI.2013.50 10.3390/e19020047 10.1111/risa.12247 10.1016/j.ress.2006.04.007 10.1016/j.ress.2010.11.005 10.1016/S0895-4356(02)00539-5 10.1016/j.ress.2018.08.010 10.1007/s13349-011-0009-5 10.1186/1471-2288-10-7 10.1109/TIE.2012.2188259 10.1111/j.1540-5907.2010.00447.x 10.1016/j.eswa.2018.08.033 10.1016/j.ress.2013.08.004 10.1080/10618600.2014.907095 10.1016/j.ress.2019.02.002 10.1016/j.ymssp.2018.07.048 10.1109/TR.2013.2285033 10.1016/j.ress.2017.10.004 10.1109/JIOT.2019.2957029 10.1016/j.ress.2019.106754 10.1006/mssp.2001.1462 10.1016/j.apenergy.2017.04.048 10.1016/j.ress.2013.02.022 10.1198/016214502388618447 10.1016/j.ress.2015.02.001 10.1016/j.asoc.2016.06.030 10.1109/TPAMI.2018.2833865 10.1016/S0888-3270(03)00041-4 10.1016/j.ress.2018.02.007 10.1016/j.neucom.2016.09.076 10.1016/j.ymssp.2005.09.012 10.1016/j.ress.2019.106727 10.1177/1475921718794953 10.1016/j.acha.2017.02.002 10.1016/j.ress.2019.02.011 10.1016/j.engappai.2019.103346 10.1016/j.ress.2018.02.013 10.1186/1471-2288-10-81 10.1016/j.ress.2019.02.001 10.1016/j.asoc.2017.08.016 10.1016/j.ejor.2010.11.018 10.1109/TR.2016.2591504 10.1016/j.jclinepi.2006.01.014 10.1109/TPAMI.2019.2914897 10.1061/(ASCE)AS.1943-5525.0000572 10.3390/machines5040021 10.1016/j.ress.2020.107257 10.1109/TKDE.2021.3079836 10.1016/j.ress.2018.02.012 10.1016/j.ress.2019.01.006 10.1016/j.ress.2016.12.003 10.1016/j.measurement.2017.02.003 10.1016/j.compbiomed.2020.104041 10.1016/j.neucom.2018.07.004 10.1016/j.ress.2018.09.018 10.1016/j.chemolab.2016.12.009 10.1016/j.jprocont.2018.05.004 10.1007/s11071-014-1857-4 10.1016/j.ress.2018.01.017 10.1016/j.solener.2018.10.054 10.1016/j.cosrev.2009.03.005 10.1016/j.ymssp.2016.10.034 10.1016/j.jnca.2015.11.016 10.1007/s10115-013-0679-x 10.1016/j.engappai.2019.03.003 10.1016/j.ress.2017.02.007 10.1016/j.renene.2018.10.062 10.1016/j.rser.2015.08.061 10.1016/j.jprocont.2019.05.012 10.4310/SII.2009.v2.n3.a8 10.1016/j.engappai.2015.08.010 10.1109/TEC.2007.914185 10.1109/TNNLS.2018.2812811 10.1016/j.ress.2018.02.003 10.1201/9781315273860 10.1109/TPAMI.2018.2843329 |
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| Keywords | Recurrent Neural Networks (RNNs), Reservoir Computing (RC) Deep Neural Networks (DNNs) Optimal Transport (OT) Prognostics and Health Management (PHM) Generative Adversarial Networks (GANs) Predictive maintenance Reservoir Computing (RC) Recurrent Neural Networks (RNNs) |
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| References | Jaeger (bib0099) 2016; 18 Vergouw (bib0128) 2010 Baraldi, Di Maio, Rigamonti, Zio, Seraoui (bib0079) 2015 Brownjohn, de Stefano, Xu, Wenzel, Aktan (bib0106) 2011 Wang, Tsui, Miao (bib0005) 2017 Cuturi, Doucet (bib0063) 2014 Pele, Werman (bib0064) 2009 Yang, Shi, Gao, Wang, Yang (bib0089) 2018 Hastie, Rosset, Zhu, Zou (bib0040) 2009 Cakir, He, Bargal, Sclaroff (bib0081) 2019 Yu, Yoo, Jang, Park, Kim (bib0042) 2018 Bengio, Courville, Vincent (bib0087) 2013 Eekhout, de Boer, Twisk, de Vet, Heymans (bib0122) 2012 Li, Wang, Zhang (bib0059) 2013 Seo, Gary, Marsh (bib0052) 2006 Duan, Makis, Deng (bib0028) 2020; 193 Li, Chen, Jin, Shi, Goh, Ng (bib0037) 2019 Tang, Parsons, Jude (bib0074) 2019; 186 Gan (bib0007) 2020 Leturiondo, Salgado, Ciani, Galar, Catelani (bib0131) 2017 Sankararaman, Goebel (bib0102) 2015 Aven, Zio (bib0143) 2011 Baraldi, Di Maio, Turati, Zio (bib0044) 2015 Jardine, Lin, Banjevic (bib0053) 2006; 20 Wan, Ni (bib0029) 2019 Downey, Lui, Hu, Laflamme, Hu (bib0121) . 2019; 182 Zhu, Lu, Yao, Dai, Hu (bib0094) 2018 Samanta, Al-Balushi (bib0101) 2003; 17 Barreno, Nelson, Joseph, Tygar (bib0154) 2010 Baraldi, Razavi-Far, Zio (bib0157) 2011; 96 Park, Kolouri, Kundu, Rohde (bib0069) 2018 Panda, Khilar (bib0070) 2012 Cao, Huang, Yang, Wang, Wang, Tan (bib0083) 2019 Listou Ellefsen, Bjørlykhaug, Æsøy, Ushakov, Zhang (bib0112) 2019; 183 Sharp, Weiss (bib0011) 2018 Huang, Yi, Li (bib0051) 2017 Baraldi, Di Maio, Rigamonti, Zio, Seraoui (bib0084) 2015; 58 Xu, Baraldi, Al-Dahidi, Zio (bib0134) 2020 Yan, Ji, Shen (bib0046) 2017 A. Karpatne, W. Watkins, J. Read, and V. Kumar, “Physics-guided neural networks (PGNN): an application in lake temperature modeling.” arXiv preprint . Costilla-Reyes, Vera-Rodriguez, Scully, Ozanyan (bib0092) 2019 Arcos Jiménez, Gómez Muñoz, García Márquez (bib0075) 2019; 184 Chen, Xu, Wang, Wang (bib0023) 2018 Liu, Zio (bib0158) 2016; 47 Montavon, Müller, Cuturi (bib0061) 2016 Zhang, Lee (bib0104) 2011 Elattar, Elminir, Riad (bib0008) 2016 Saltelli, Tarantola (bib0145) 2002 Ribeiro, Singh, Guestrin (bib0140) 2016 Deng, Di Bucchianico, Pechenizkiy (bib0161) 2020; 196 Siryani, Tanju, Eveleigh (bib0018) 2017 R. Tipireddy, P. Perdikaris, P. Stinis, & A. Tartakovsky (2019). A comparative study of physics-informed neural network models for learning unknown dynamics and constitutive relations. arXiv preprint García Nieto, García-Gonzalo, Sánchez Lasheras, De Cos Juez (bib0116) 2015; 138 Bangalore, Letzgus, Karlsson, Patriksson (bib0047) 2017 Kwon, Hodkiewicz, Fan, Shibutani, Pecht (bib0017) 2016 He, Dai, Lu, Mou (bib0111) 2018; 180 Borgonovo, Plischke (bib0146) 2016 Al-Dahidi, Di Maio, Baraldi, Zio (bib0159) 2017; 231 Honaker, King (bib0129) 2010; 54 Ahmed, Mahmood, Hu (bib0036) 2016 Joe Qin (bib0041) 2009 Nguyen, Medjaher (bib0113) 2019; 188 Dumargue, Pougeon, Massé (bib0010) 2016 King, Titchener-Hooker, Zhou (bib0144) 2007 Chen, Peng, Zhu, Li (bib0138) 2020 Garcia-Alvarez (bib0071) 2009 Tamilselvan, Wang (bib0073) 2013; 115 Lei, Jia, Lin, Xing, Ding (bib0095) 2016; 63 Goldstein, Kapelner, Bleich, Pitkin (bib0152) 2015 Yang, Tang, Yao (bib0135) 2008 Saxena, Celaya, Saha, Saha, Goebel (bib0016) 2010; 1 Wang, Tang, Yue, Zhou, Deonauth (bib0054) 2018 Marugán, Chacón, Márquez (bib0034) 2019; 191 L. Biggio, A. Wieland, M.A. Chao, I. Kastanis, & O. Fink (2021). Uncertainty-aware remaining useful life predictor. arXiv preprint Yin, Ding, Xie, Luo (bib0093) 2014 Li, Zhang, Ding (bib0109) 2019; 182 Zhang, Wang, Li, Gao (bib0090) 2019 Shi, Cao, Zhang, Li, Xu (bib0020) 2016 Xu, Baraldi, Lu, Cannarile, Zio (bib0039) 2019 Kammammettu, Li (bib0067) 2019 Clark, Altman (bib0133) 2003 Jennions, Niculita, Esperon-Miguez (bib0009) 2016; 7 Zeng, Di Maio, Zio, Kang (bib0015) 2017; 231 Mujica, Rodellar, Fernández, Güemes (bib0050) 2011 Zio (bib0001) 2016 Zio (bib0025) 2009 Xu, Sun, Wan, Liu, Song (bib0078) 2017 Caesarendra, Tjahjowidodo (bib0057) 2017 Han, Yu, Song, Tang, Dai (bib0012) 2019 Flage, Aven, Zio, Baraldi (bib0156) 2014; 34 Tahan, Tsoutsanis, Muhammad, Abdul Karim (bib0004) 2017 Marshall, Altman, Royston, Holder (bib0132) 2010 Friedman (bib0151) 2001 Sankararaman, Mahadevan, Orchard (bib0155) 2020; 6 L. Von Rueden et al., “Informed machine learning-a taxonomy and survey of integrating knowledge into learning systems.” IEEE Transactions on Knowledge and Data Engineering preprint pp.1-1, doi: 10.1109/TKDE.2021.3079836. Manjurul Islam, Kim (bib0076) 2019; 184 Li, Ding, Sun (bib0110) 2018 Ng, Cabrera, Tse, Chen, Tsui (bib0066) 2015 Shi, Chehade (bib0114) 2021; 205 Odgaard, Lin, Jorgensen (bib0048) 2008 Yang, Liu, Zeng, Xie (bib0049) 2019 Chang, Fang (bib0120) 2019; 186 Štrumbelj, Kononenko (bib0142) 2014 Xing, Liu (bib0038) 2019; 55 Han, Liu, Wu, Sarkar, Jiang (bib0096) 2019 Raj, Murali (bib0056) 2013 Donders, van der Heijden, Stijnen, Moons (bib0127) 2006 Hu, Xie, Ng, Levitin (bib0033) 2007; 92 Lei, Li, Guo, Li, Yan, Lin (bib0003) 2018 Ramdas, Trillos, Cuturi (bib0065) 2017 Gretton, Borgwardt, Rasch, Schölkopf, Smola (bib0062) 2012 Baptista, Henriques, de Medeiros, Malere, Nascimento, Prendinger (bib0118) 2019; 184 Di Maio, Baraldi, Zio, Seraoui (bib0043) 2013 Benker, Furtner, Semm, Zaeh (bib0163) 2020 Marseguerra (bib0031) 2014; 129 Reder, Yürüşen, Melero (bib0086) 2018; 169 Xiao, Huang, Pan, Liu, Song (bib0130) 2017 Lundberg, Lee (bib0137) 2017 Daigle, Goebel (bib0103) 2011; 2 Wang, Baraldi, Lu, Zio (bib0068) 2020 Razavi-Far, Chakrabarti, Saif, Zio (bib0125) 2019 Chen, Jing, Chang, Liu (bib0115) 2019; 185 Bian, Yang, Huang, Xu, Liu, Zio (bib0085) 2019; 188 Papernot, McDaniel, Goodfellow, Jha, Celik, Swami (bib0153) 2016 Arn, Narayana, Draper, Emerson, Kirby, Peterson (bib0080) 2018 Kankar, Sharma, Harsha (bib0055) 2011; 11 Tolo, Silvia, Tian, Bausch, Becerra, Santhosh, Vinod (bib0032) 2019; 186 Di Maio, Turati, Z (bib0014) 2016 Qian, Zhang (bib0100) 2018; 70 Jeong Haedong, Park, Park (bib0027) 2019; 184 Raissi, Perdikaris, Karniadakis (bib0148) 2019; 378 Liu, Zhang, Niu, Yao, Wu (bib0139) 2020 Sekhar (bib0026) 2004 Kolouri, Park, Thorpe, Slepcev, Rohde (bib0058) 2017 Compare, Baraldi, Zio (bib0006) 2020 Xu, Baraldi, Zio (bib0097) 2020 Zheng, Li, Yan, Tang, Tan (bib0082) 2018 Yang, Sun, Ye (bib0013) 2020 Liu, Zhou, Xu, Zheng, Peng, Jiang (bib0022) 2018 Si, Wang, Hu, Zhou (bib0107) 2011 Li, Zia, Tran, Yu, Hager, Chandraker (bib0091) 2019 Zhao, Liang, Wang, Lu (bib0117) 2017; 164 Liu, Li, Zio (bib0035) 2017; 87 Fink, Zio, Weidmann (bib0072) 2014; 121 Yoon, Hwang (bib0136) 1995 Lukoševičius, Jaeger (bib0098) 2009; 3 Liu, Hu, Zhang (bib0108) 2019; 185 Zhang, Baraldi, Cadet, Yousfi-Steiner, Bérenguer, Zio (bib0160) 2019; 124 Kandukuri, Klausen, Karimi, Robbersmyr (bib0002) 2016 Peng, Ye, Chen (bib0162) 2019; 67 Lin, Lu, Chen, Zhou, Sun (bib0021) 2019 Goebel (bib0024) 2017 Liu, Zio (bib0105) 2017; 160 Robles-Velasco, Cortés, Muñuzuri, Onieva (bib0077) 2020; 196 Ranjbar, Moradi, Azami, Jalili (bib0123) 2015 Rabin, Ferradans, Papadakis (bib0060) 2014 Li, Wen (bib0045) 2014 Winnig (bib0019) 2016; 57 Cheng, Chan (bib0124) 2019; 81 Ahmad, Khan, Islam, Kim (bib0119) 2019; 184 Baraldi, Enders (bib0126) 2010 Magesh, Myloth, Tom (bib0141) 2020 Quintanilha, Elias, da Silva, Fonini, da Silva, Netto (bib0030) 2021 Lei, Jia, Lin, Xing, Ding (bib0088) 2016 Liu (10.1016/j.ress.2021.108119_bib0105) 2017; 160 Gretton (10.1016/j.ress.2021.108119_bib0062) 2012 Magesh (10.1016/j.ress.2021.108119_bib0141) 2020 Odgaard (10.1016/j.ress.2021.108119_bib0048) 2008 Shi (10.1016/j.ress.2021.108119_bib0114) 2021; 205 Benker (10.1016/j.ress.2021.108119_bib0163) 2020 Reder (10.1016/j.ress.2021.108119_bib0086) 2018; 169 Yan (10.1016/j.ress.2021.108119_bib0046) 2017 Costilla-Reyes (10.1016/j.ress.2021.108119_bib0092) 2019 Rabin (10.1016/j.ress.2021.108119_bib0060) 2014 Ribeiro (10.1016/j.ress.2021.108119_bib0140) 2016 Xu (10.1016/j.ress.2021.108119_bib0097) 2020 Wang (10.1016/j.ress.2021.108119_bib0054) 2018 Cheng (10.1016/j.ress.2021.108119_bib0124) 2019; 81 Saxena (10.1016/j.ress.2021.108119_bib0016) 2010; 1 Marseguerra (10.1016/j.ress.2021.108119_bib0031) 2014; 129 Samanta (10.1016/j.ress.2021.108119_bib0101) 2003; 17 Lei (10.1016/j.ress.2021.108119_bib0003) 2018 Eekhout (10.1016/j.ress.2021.108119_bib0122) 2012 Ahmed (10.1016/j.ress.2021.108119_bib0036) 2016 Baptista (10.1016/j.ress.2021.108119_bib0118) 2019; 184 Yang (10.1016/j.ress.2021.108119_bib0089) 2018 Liu (10.1016/j.ress.2021.108119_bib0108) 2019; 185 Saltelli (10.1016/j.ress.2021.108119_bib0145) 2002 Han (10.1016/j.ress.2021.108119_bib0096) 2019 Aven (10.1016/j.ress.2021.108119_bib0143) 2011 Yang (10.1016/j.ress.2021.108119_bib0049) 2019 Zhang (10.1016/j.ress.2021.108119_bib0090) 2019 Chen (10.1016/j.ress.2021.108119_bib0138) 2020 Kolouri (10.1016/j.ress.2021.108119_bib0058) 2017 Ng (10.1016/j.ress.2021.108119_bib0066) 2015 Di Maio (10.1016/j.ress.2021.108119_bib0043) 2013 Zhao (10.1016/j.ress.2021.108119_bib0117) 2017; 164 Nguyen (10.1016/j.ress.2021.108119_bib0113) 2019; 188 Donders (10.1016/j.ress.2021.108119_bib0127) 2006 Yang (10.1016/j.ress.2021.108119_bib0013) 2020 Friedman (10.1016/j.ress.2021.108119_bib0151) 2001 Jardine (10.1016/j.ress.2021.108119_bib0053) 2006; 20 Chen (10.1016/j.ress.2021.108119_bib0023) 2018 Liu (10.1016/j.ress.2021.108119_bib0035) 2017; 87 Baraldi (10.1016/j.ress.2021.108119_bib0084) 2015; 58 Li (10.1016/j.ress.2021.108119_bib0059) 2013 Goldstein (10.1016/j.ress.2021.108119_bib0152) 2015 Xiao (10.1016/j.ress.2021.108119_bib0130) 2017 Lukoševičius (10.1016/j.ress.2021.108119_bib0098) 2009; 3 Montavon (10.1016/j.ress.2021.108119_bib0061) 2016 Sankararaman (10.1016/j.ress.2021.108119_bib0102) 2015 Winnig (10.1016/j.ress.2021.108119_bib0019) 2016; 57 Xu (10.1016/j.ress.2021.108119_bib0078) 2017 Liu (10.1016/j.ress.2021.108119_bib0158) 2016; 47 Li (10.1016/j.ress.2021.108119_bib0110) 2018 Qian (10.1016/j.ress.2021.108119_bib0100) 2018; 70 Compare (10.1016/j.ress.2021.108119_bib0006) 2020 Kandukuri (10.1016/j.ress.2021.108119_bib0002) 2016 Di Maio (10.1016/j.ress.2021.108119_bib0014) 2016 Caesarendra (10.1016/j.ress.2021.108119_bib0057) 2017 Ahmad (10.1016/j.ress.2021.108119_bib0119) 2019; 184 Leturiondo (10.1016/j.ress.2021.108119_bib0131) 2017 Siryani (10.1016/j.ress.2021.108119_bib0018) 2017 Xu (10.1016/j.ress.2021.108119_bib0039) 2019 Mujica (10.1016/j.ress.2021.108119_bib0050) 2011 Robles-Velasco (10.1016/j.ress.2021.108119_bib0077) 2020; 196 He (10.1016/j.ress.2021.108119_bib0111) 2018; 180 Wang (10.1016/j.ress.2021.108119_bib0005) 2017 Deng (10.1016/j.ress.2021.108119_bib0161) 2020; 196 Baraldi (10.1016/j.ress.2021.108119_bib0157) 2011; 96 Zhang (10.1016/j.ress.2021.108119_bib0160) 2019; 124 Duan (10.1016/j.ress.2021.108119_bib0028) 2020; 193 King (10.1016/j.ress.2021.108119_bib0144) 2007 Tamilselvan (10.1016/j.ress.2021.108119_bib0073) 2013; 115 Tolo (10.1016/j.ress.2021.108119_bib0032) 2019; 186 Tang (10.1016/j.ress.2021.108119_bib0074) 2019; 186 Dumargue (10.1016/j.ress.2021.108119_bib0010) 2016 García Nieto (10.1016/j.ress.2021.108119_bib0116) 2015; 138 Fink (10.1016/j.ress.2021.108119_bib0072) 2014; 121 Seo (10.1016/j.ress.2021.108119_bib0052) 2006 Kwon (10.1016/j.ress.2021.108119_bib0017) 2016 Yin (10.1016/j.ress.2021.108119_bib0093) 2014 Honaker (10.1016/j.ress.2021.108119_bib0129) 2010; 54 Pele (10.1016/j.ress.2021.108119_bib0064) 2009 Zio (10.1016/j.ress.2021.108119_bib0025) 2009 Lin (10.1016/j.ress.2021.108119_bib0021) 2019 Lei (10.1016/j.ress.2021.108119_bib0095) 2016; 63 10.1016/j.ress.2021.108119_bib0150 Barreno (10.1016/j.ress.2021.108119_bib0154) 2010 Huang (10.1016/j.ress.2021.108119_bib0051) 2017 Jaeger (10.1016/j.ress.2021.108119_bib0099) 2016; 18 Yu (10.1016/j.ress.2021.108119_bib0042) 2018 Wang (10.1016/j.ress.2021.108119_bib0068) 2020 Goebel (10.1016/j.ress.2021.108119_bib0024) 2017 Joe Qin (10.1016/j.ress.2021.108119_bib0041) 2009 Li (10.1016/j.ress.2021.108119_bib0037) 2019 Arcos Jiménez (10.1016/j.ress.2021.108119_bib0075) 2019; 184 Baraldi (10.1016/j.ress.2021.108119_bib0079) 2015 Ramdas (10.1016/j.ress.2021.108119_bib0065) 2017 Sekhar (10.1016/j.ress.2021.108119_bib0026) 2004 Elattar (10.1016/j.ress.2021.108119_bib0008) 2016 Li (10.1016/j.ress.2021.108119_bib0109) 2019; 182 Chen (10.1016/j.ress.2021.108119_bib0115) 2019; 185 Chang (10.1016/j.ress.2021.108119_bib0120) 2019; 186 Sharp (10.1016/j.ress.2021.108119_bib0011) 2018 Arn (10.1016/j.ress.2021.108119_bib0080) 2018 Li (10.1016/j.ress.2021.108119_bib0091) 2019 Raissi (10.1016/j.ress.2021.108119_bib0148) 2019; 378 Clark (10.1016/j.ress.2021.108119_bib0133) 2003 Si (10.1016/j.ress.2021.108119_bib0107) 2011 Kammammettu (10.1016/j.ress.2021.108119_bib0067) 2019 Bengio (10.1016/j.ress.2021.108119_bib0087) 2013 Downey (10.1016/j.ress.2021.108119_bib0121) 2019; 182 Xu (10.1016/j.ress.2021.108119_bib0134) 2020 Liu (10.1016/j.ress.2021.108119_bib0022) 2018 Cakir (10.1016/j.ress.2021.108119_bib0081) 2019 Jeong Haedong (10.1016/j.ress.2021.108119_bib0027) 2019; 184 10.1016/j.ress.2021.108119_bib0147 Garcia-Alvarez (10.1016/j.ress.2021.108119_bib0071) 2009 Štrumbelj (10.1016/j.ress.2021.108119_bib0142) 2014 Park (10.1016/j.ress.2021.108119_bib0069) 2018 Hastie (10.1016/j.ress.2021.108119_bib0040) 2009 10.1016/j.ress.2021.108119_bib0149 Jennions (10.1016/j.ress.2021.108119_bib0009) 2016; 7 Gan (10.1016/j.ress.2021.108119_bib0007) 2020 Han (10.1016/j.ress.2021.108119_bib0012) 2019 Xing (10.1016/j.ress.2021.108119_bib0038) 2019; 55 Baraldi (10.1016/j.ress.2021.108119_bib0126) 2010 Zio (10.1016/j.ress.2021.108119_bib0001) 2016 Zeng (10.1016/j.ress.2021.108119_bib0015) 2017; 231 Vergouw (10.1016/j.ress.2021.108119_bib0128) 2010 Zhu (10.1016/j.ress.2021.108119_bib0094) 2018 Panda (10.1016/j.ress.2021.108119_bib0070) 2012 Kankar (10.1016/j.ress.2021.108119_bib0055) 2011; 11 Razavi-Far (10.1016/j.ress.2021.108119_bib0125) 2019 Lundberg (10.1016/j.ress.2021.108119_bib0137) 2017 Tahan (10.1016/j.ress.2021.108119_bib0004) 2017 Li (10.1016/j.ress.2021.108119_bib0045) 2014 Marugán (10.1016/j.ress.2021.108119_bib0034) 2019; 191 Bian (10.1016/j.ress.2021.108119_bib0085) 2019; 188 Ranjbar (10.1016/j.ress.2021.108119_bib0123) 2015 Listou Ellefsen (10.1016/j.ress.2021.108119_bib0112) 2019; 183 Peng (10.1016/j.ress.2021.108119_bib0162) 2019; 67 Hu (10.1016/j.ress.2021.108119_bib0033) 2007; 92 Cao (10.1016/j.ress.2021.108119_bib0083) 2019 Yang (10.1016/j.ress.2021.108119_bib0135) 2008 Baraldi (10.1016/j.ress.2021.108119_bib0044) 2015 Daigle (10.1016/j.ress.2021.108119_bib0103) 2011; 2 Sankararaman (10.1016/j.ress.2021.108119_bib0155) 2020; 6 Lei (10.1016/j.ress.2021.108119_bib0088) 2016 Liu (10.1016/j.ress.2021.108119_bib0139) 2020 Zheng (10.1016/j.ress.2021.108119_bib0082) 2018 Wan (10.1016/j.ress.2021.108119_bib0029) 2019 Raj (10.1016/j.ress.2021.108119_bib0056) 2013 Zhang (10.1016/j.ress.2021.108119_bib0104) 2011 Brownjohn (10.1016/j.ress.2021.108119_bib0106) 2011 Yoon (10.1016/j.ress.2021.108119_bib0136) 1995 Manjurul Islam (10.1016/j.ress.2021.108119_bib0076) 2019; 184 Bangalore (10.1016/j.ress.2021.108119_bib0047) 2017 Papernot (10.1016/j.ress.2021.108119_bib0153) 2016 Quintanilha (10.1016/j.ress.2021.108119_bib0030) 2021 Flage (10.1016/j.ress.2021.108119_bib0156) 2014; 34 10.1016/j.ress.2021.108119_bib0164 Cuturi (10.1016/j.ress.2021.108119_bib0063) 2014 Borgonovo (10.1016/j.ress.2021.108119_bib0146) 2016 Shi (10.1016/j.ress.2021.108119_bib0020) 2016 Marshall (10.1016/j.ress.2021.108119_bib0132) 2010 Al-Dahidi (10.1016/j.ress.2021.108119_bib0159) 2017; 231 |
| References_xml | – year: 2015 ident: bib0066 article-title: Distance-based analysis of dynamical systems reconstructed from vibrations for bearing diagnostics publication-title: Nonlinear Dyn – year: 2016 ident: bib0008 article-title: Prognostics: a literature review publication-title: Complex & Intell Syst – year: 2017 ident: bib0130 article-title: Fault diagnosis and prognosis of wastewater processes with incomplete data by the auto-associative neural networks and ARMA model publication-title: Chemom Intell Lab Syst – volume: 378 start-page: 686 year: 2019 end-page: 707 ident: bib0148 article-title: Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations publication-title: J Comput Phys – year: 2018 ident: bib0054 article-title: Study on nonlinear dynamic characteristics inherent in offshore jacket platform using long-term monitored response of ice-structure interaction publication-title: Appl Ocean Res – reference: L. Biggio, A. Wieland, M.A. Chao, I. Kastanis, & O. Fink (2021). Uncertainty-aware remaining useful life predictor. arXiv preprint – year: 2019 ident: bib0049 article-title: Real-time condition monitoring and fault detection of components based on machine-learning reconstruction model publication-title: Renew Energy – start-page: 703 year: 2019 end-page: 716 ident: bib0037 article-title: MAD-GAN: Multivariate anomaly detection for time series data with generative adversarial networks – volume: 186 start-page: 51 year: 2019 end-page: 63 ident: bib0120 article-title: A hybrid prognostic method for system degradation based on particle filter and relevance vector machine publication-title: Reliab Eng Syst Saf – volume: 231 start-page: 36 year: 2017 end-page: 52 ident: bib0015 article-title: A hierarchical decision-making framework for the assessment of the prediction capability of prognostic methods publication-title: J Risk Reliab – volume: 185 start-page: 502 year: 2019 end-page: 510 ident: bib0108 article-title: Remaining useful life prediction based on health index similarity publication-title: Reliab Eng Syst Saf – year: 2019 ident: bib0021 article-title: Unsupervised deep learning of compact binary descriptors publication-title: IEEE Trans Pattern Anal Mach Intell – volume: 121 start-page: 198 year: 2014 end-page: 206 ident: bib0072 article-title: Predicting component reliability and level of degradation with complex-valued neural networks publication-title: Reliab Eng Syst Saf – year: 2018 ident: bib0094 article-title: Fault diagnosis approach for photovoltaic arrays based on unsupervised sample clustering and probabilistic neural network model publication-title: Sol Energy – reference: L. Von Rueden et al., “Informed machine learning-a taxonomy and survey of integrating knowledge into learning systems.” IEEE Transactions on Knowledge and Data Engineering preprint pp.1-1, doi: 10.1109/TKDE.2021.3079836. – year: 2009 ident: bib0064 article-title: Fast and robust earth mover's distances publication-title: Proceedings of the IEEE international conference on computer vision – volume: 182 start-page: 1 year: . 2019 end-page: 12 ident: bib0121 article-title: Physics-based prognostics of lithium-ion battery using non-linear least squares with dynamic bounds publication-title: Reliab Eng Syst Saf – year: 2007 ident: bib0144 article-title: Ranking bioprocess variables using global sensitivity analysis: a case study in centrifugation publication-title: Bioprocess Biosyst Eng – year: 2019 ident: bib0029 article-title: Bayesian multi-task learning methodology for reconstruction of structural health monitoring data publication-title: Struct Health Monit – year: 2015 ident: bib0123 article-title: An imputation-based matrix factorization method for improving accuracy of collaborative filtering systems publication-title: Eng Appl Artif Intell – year: 2012 ident: bib0062 article-title: A kernel two-sample test publication-title: J Mach Learn Res – volume: 183 start-page: 240 year: 2019 end-page: 251 ident: bib0112 article-title: Remaining useful life predictions for turbofan engine degradation using semi-supervised deep architecture publication-title: Reliab Eng Syst Saf – year: 2020 ident: bib0006 article-title: Challenges to IoT-enabled predictive maintenance for industry 4.0 publication-title: IEEE Internet Things J – year: 2020 ident: bib0139 article-title: Intelligent prognostics of machining tools based on adaptive variational mode decomposition and deep learning method with attention mechanism publication-title: Neurocomputing – volume: 124 start-page: 479 year: 2019 end-page: 501 ident: bib0160 article-title: An ensemble of models for integrating dependent sources of information for the prognosis of the remaining useful life of proton exchange membrane fuel cells publication-title: Mech Syst Signal Process – volume: 185 start-page: 372 year: 2019 end-page: 382 ident: bib0115 article-title: Gated recurrent unit based recurrent neural network for remaining useful life prediction of nonlinear deterioration process publication-title: Reliab Eng Syst Saf – volume: 186 start-page: 110 year: 2019 end-page: 119 ident: bib0032 article-title: Robust on-line diagnosis tool for the early accident detection in nuclear power plants publication-title: Reliab Eng Syst Saf – year: 2021 ident: bib0030 article-title: A fault detector/classifier for closed-ring power generators using machine learning publication-title: Reliab Eng Syst Saf – year: 2018 ident: bib0080 article-title: Motion segmentation via generalized curvatures publication-title: IEEE Trans Pattern Anal Mach Intell – volume: 63 start-page: 3137 year: 2016 end-page: 3147 ident: bib0095 article-title: An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data publication-title: IEEE Trans Ind Electron – year: 2008 ident: bib0135 article-title: Self-adaptive differential evolution with neighborhood search publication-title: Proceedings of the 2008 IEEE congress on evolutionary computation, CEC 2008 – reference: R. Tipireddy, P. Perdikaris, P. Stinis, & A. Tartakovsky (2019). A comparative study of physics-informed neural network models for learning unknown dynamics and constitutive relations. arXiv preprint – volume: 96 start-page: 480 year: 2011 end-page: 488 ident: bib0157 article-title: Classifier-ensemble incremental-learning procedure for nuclear transient identification at different operational conditions publication-title: Reliab Eng Syst Saf – year: 2016 ident: bib0061 article-title: Wasserstein training of restricted boltzmann machines publication-title: Adv Neural Inf Process Syst – start-page: 4768 year: 2017 end-page: 4777 ident: bib0137 article-title: A unified approach to interpreting model predictions publication-title: Proceedings of the 31st international conference on neural information processing systems – year: 2019 ident: bib0067 article-title: Change point and fault detection using Kantorovich distance publication-title: J Process Control – year: 2008 ident: bib0048 article-title: Observer and data-driven-model-based fault detection in power plant coal mills publication-title: IEEE Trans Energy Convers – year: 2020 ident: bib0138 article-title: A novel deep learning method based on attention mechanism for bearing remaining useful life prediction publication-title: Applied Soft Computing Journal – year: 2017 ident: bib0051 article-title: Sensor fault diagnosis for structural health monitoring based on statistical hypothesis test and missing variable approach publication-title: J Aerosp Eng – year: 2006 ident: bib0052 article-title: A review and comparison of methods for detecting outliersin univariate data sets publication-title: Dep Biostat Grad Sch Public Health – volume: 184 start-page: 2 year: 2019 end-page: 12 ident: bib0075 article-title: Dirt and mud detection and diagnosis on a wind turbine blade employing guided waves and supervised learning classifiers publication-title: Reliab Eng Syst Saf – year: 2012 ident: bib0122 article-title: Missing data: a systematic review of how they are reported and handled publication-title: Epidemiology – year: 2010 ident: bib0128 article-title: The search for stable prognostic models in multiple imputed data sets publication-title: BMC Med Res Methodol – year: 2018 ident: bib0022 article-title: Unsupervised fault diagnosis of rolling bearings using a deep neural network based on generative adversarial networks publication-title: Neurocomputing – year: 2018 ident: bib0069 article-title: The cumulative distribution transform and linear pattern classification publication-title: Appl Comput Harmon Anal – year: 2019 ident: bib0096 article-title: An adaptive spatiotemporal feature learning approach for fault diagnosis in complex systems publication-title: Mech Syst Signal Process – year: 2001 ident: bib0151 article-title: Greedy function approximation: a gradient boosting machine publication-title: Ann Stat – year: 2015 ident: bib0044 article-title: Robust signal reconstruction for condition monitoring of industrial components via a modified auto associative kernel regression method publication-title: Mech Syst Signal Process – year: 2019 ident: bib0090 article-title: Deep latent low-rank representation for face sketch synthesis publication-title: IEEE Trans Neural Netw Learn Syst – year: 2013 ident: bib0056 article-title: Early classification of bearing faults using morphological operators and fuzzy inference publication-title: IEEE Trans Ind Electron – volume: 67 start-page: 2283 year: 2019 end-page: 2293 ident: bib0162 article-title: Bayesian deep-learning-based health prognostics toward prognostics uncertainty publication-title: IEEE Trans Ind Electron – year: 2016 ident: bib0020 article-title: Edge computing: vision and challenges publication-title: IEEE Int Things J – year: 2017 ident: bib0024 article-title: Prognostics: the science of making predictions – year: 2010 ident: bib0126 article-title: An introduction to modern missing data analyses publication-title: J Sch Psychol – year: 2006 ident: bib0127 article-title: Review: a gentle introduction to imputation of missing values publication-title: J Clin Epidemiol – year: 2009 ident: bib0041 article-title: Data-driven fault detection and diagnosis for complex industrial processes publication-title: IFAC Proceedingsof the Volumes (IFAC-PapersOnline) – year: 2017 ident: bib0078 article-title: Industrial big data for fault diagnosis: taxonomy, review, and applications publication-title: IEEE Access – volume: 182 start-page: 208 year: 2019 end-page: 218 ident: bib0109 article-title: Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction publication-title: Reliab Eng Syst Saf – volume: 70 start-page: 1034 year: 2018 end-page: 1041 ident: bib0100 article-title: A simple feedforward convolutional conceptor neural network for classification publication-title: Appl Soft Comput – year: 2019 ident: bib0092 article-title: Analysis of Spatio-temporal representations for robust footstep recognition with deep residual neural networks publication-title: IEEE Trans Pattern Anal Mach Intell – volume: 184 start-page: 27 year: 2019 end-page: 40 ident: bib0027 article-title: Hyungcheol Min, “Fault detection and identification method using observer-based residuals publication-title: Reliab Eng System Saf – year: 2011 ident: bib0143 article-title: Some considerations on the treatment of uncertainties in risk assessment for practical decision making publication-title: Reliab Eng Syst Saf – volume: 20 start-page: 1483 year: 2006 end-page: 1510 ident: bib0053 article-title: A review on machinery diagnostics and prognostics implementing condition-based maintenance publication-title: Mech Syst Signal Process – year: 2016 ident: bib0140 article-title: ‘Why should i trust you?’ Explaining the predictions of any classifier publication-title: Proceedings of the ACM SIGKDD international conference on knowledge discovery and data Mining – year: 2019 ident: bib0083 article-title: Feedback convolutional neural network for visual localization and segmentation publication-title: IEEE Trans Pattern Anal Mach Intell – year: 2010 ident: bib0132 article-title: Comparison of techniques for handling missing covariate data within prognostic modelling studies: a simulation study publication-title: BMC Med Res Methodol – volume: 196 year: 2020 ident: bib0161 article-title: Controlling the accuracy and uncertainty trade-off in RUL prediction with a surrogate wiener propagation model publication-title: Reliab Eng Syst Saf – year: 2018 ident: bib0042 article-title: A novel hybrid of auto-associative kernel regression and dynamic independent component analysis for fault detection in nonlinear multimode processes publication-title: J Process Control – volume: 129 start-page: 1 year: 2014 end-page: 9 ident: bib0031 article-title: Early detection of gradual concept drifts by text categorization and support Vector Machine techniques: the TRIO algorithm publication-title: Reliab Eng Syst Saf – year: 2019 ident: bib0091 article-title: Deep supervision with intermediate concepts publication-title: IEEE Trans Pattern Anal Mach Intell – year: 2017 ident: bib0018 article-title: A machine learning decision-support system improves the internet of things’ smart meter operations publication-title: IEEE Int Things J – year: 2017 ident: bib0004 article-title: Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: a review publication-title: Appl Energy – year: 2017 ident: bib0047 article-title: An artificial neural network-based condition monitoring method for wind turbines, with application to the monitoring of the gearbox publication-title: Wind Energy – volume: 87 start-page: 401 year: 2017 end-page: 409 ident: bib0035 article-title: A SVM framework for fault detection of the braking system in a high speed train publication-title: Mech Syst Signal Process – volume: 180 start-page: 385 year: 2018 end-page: 393 ident: bib0111 article-title: Developing ladder network for intelligent evaluation system: case of remaining useful life prediction for centrifugal pumps publication-title: Reliability Eng Syst Saf – volume: 1 start-page: 4 year: 2010 end-page: 23 ident: bib0016 article-title: Metrics for offline evaluation of prognostic performance publication-title: Int J Progn Health Manag – volume: 193 year: 2020 ident: bib0028 article-title: A two-level Bayesian early fault detection for mechanical equipment subject to dependent failure modes publication-title: Reliab Eng Syst Saf – year: 2014 ident: bib0060 article-title: Adaptive color transfer with relaxed optimal transport publication-title: 2014 IEEE international conference on image processing, ICIP 2014 – volume: 160 start-page: 21 year: 2017 end-page: 36 ident: bib0105 article-title: System dynamic reliability assessment and failure prognostics publication-title: Reliab Eng Syst Saf – volume: 115 start-page: 124 year: 2013 end-page: 135 ident: bib0073 article-title: Failure diagnosis using deep belief learning based health state classification publication-title: Reliab Eng Syst Saf – year: 2018 ident: bib0082 article-title: Sparse Temporal encoding of visual features for robust object recognition by spiking neurons publication-title: IEEE Trans Neural Netw Learn Syst – year: 2019 ident: bib0125 article-title: An integrated imputation-prediction scheme for prognostics of battery data with missing observations publication-title: Expert Syst Appl – year: 2015 ident: bib0079 article-title: Unsupervised clustering of vibration signals for identifying anomalous conditions in a nuclear turbine publication-title: J Intell Fuzzy Syst – reference: A. Karpatne, W. Watkins, J. Read, and V. Kumar, “Physics-guided neural networks (PGNN): an application in lake temperature modeling.” arXiv preprint – year: 2012 ident: bib0070 article-title: Distributed soft fault detection algorithm in wireless sensor networks using statistical test publication-title: Proceedings of the 2012 2nd IEEE international conference on parallel, distributed and grid computing, PDGC 2012 – year: 2020 ident: bib0013 article-title: Designing mission abort strategies based on early-warning information: application to UAV publication-title: IEEE Trans Ind Inf – year: 2016 ident: bib0017 article-title: IoT-Based prognostics and systems health management for industrial applications publication-title: IEEE Access – volume: 186 start-page: 24 year: 2019 end-page: 36 ident: bib0074 article-title: Comparison of automatic and guided learning for Bayesian networks to analyse pipe failures in the water distribution system publication-title: Reliab Eng Syst Saf – year: 2017 ident: bib0131 article-title: Architecture for hybrid modelling and its application to diagnosis and prognosis with missing data publication-title: Meas J Int Meas Confed – year: 2011 ident: bib0050 article-title: Q-statistic and t2-statistic pca-based measures for damage assessment in structures publication-title: Struct Health Monit – year: 2019 ident: bib0081 article-title: Hashing with mutual information publication-title: IEEE Trans Pattern Anal Mach Intell – volume: 138 start-page: 219 year: 2015 end-page: 231 ident: bib0116 article-title: Hybrid PSO-SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability publication-title: Reliab Eng Syst Saf – volume: 184 start-page: 228 year: 2019 end-page: 239 ident: bib0118 article-title: Remaining useful life estimation in aeronautics: combining data-driven and Kalman filtering publication-title: Reliab Eng Syst Saf – volume: 184 start-page: 67 year: 2019 end-page: 76 ident: bib0119 article-title: A reliable technique for remaining useful life estimation of rolling element bearings using dynamic regression models publication-title: Reliab Eng Syst Saf – year: 2014 ident: bib0142 article-title: Explaining prediction models and individual predictions with feature contributions publication-title: Knowl Inf Syst – year: 2019 ident: bib0039 article-title: Anomaly detection for industrial systems using generative adversarial networks publication-title: Proceedings of the 4th International Conference on System Reliability and Safety (ICSRS 2019) – year: 2020 ident: bib0068 article-title: Fault detection based on optimal transport theory publication-title: Proceedings of the ESREL 2020 – PSAM 15 – volume: 54 start-page: 561 year: 2010 end-page: 581 ident: bib0129 article-title: What to do about missing values in time-series cross-section data publication-title: Am J Pol Sci – year: 2016 ident: bib0010 article-title: An approach to designing PHM systems with systems engineering publication-title: Proceedings of the European conference of the Prognostics and health management society – year: 2015 ident: bib0102 article-title: Uncertainty in prognostics and systems health management publication-title: Int J Progn Health Manag – volume: 17 start-page: 317 year: 2003 end-page: 328 ident: bib0101 article-title: Artificial neural network based fault diagnostics of rolling element bearings using time-domain features publication-title: Mech Syst Signal Process – volume: 205 year: 2021 ident: bib0114 article-title: A dual-LSTM framework combining change point detection and remaining useful life prediction publication-title: Reliab Eng Syst Saf – year: 2010 ident: bib0154 article-title: The security of machine learning publication-title: Mach Learn – year: 2014 ident: bib0093 article-title: A review on basic data-driven approaches for industrial process monitoring publication-title: IEEE Trans Ind Electron – year: 2017 ident: bib0057 article-title: A review of feature extraction methods in vibration-based condition monitoring and its application for degradation trend estimation of low-speed slew bearing publication-title: Machines – year: 2016 ident: bib0036 article-title: A survey of network anomaly detection techniques publication-title: J Netw Comput Appl – year: 2009 ident: bib0071 article-title: Fault detection using principal component Analysis (PCA) in a wastewater treatment plant (WWTP) publication-title: Proceedings of the 62th international student's scientific conference – year: 2016 ident: bib0088 article-title: An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big Data publication-title: IEEE Trans Ind Electron – volume: 58 start-page: 160 year: 2015 end-page: 178 ident: bib0084 article-title: Clustering for unsupervised fault diagnosis in nuclear turbine shut-down transients publication-title: Mech Syst Signal Process – volume: 47 start-page: 553 year: 2016 end-page: 564 ident: bib0158 article-title: A SVR-based ensemble approach for drifting data streams with recurring patterns publication-title: Appl Soft Comput – volume: 3 start-page: 127 year: 2009 end-page: 149 ident: bib0098 article-title: Reservoir computing approaches to recurrent neural network training publication-title: Comput Sci Rev – volume: 11 start-page: 2300 year: 2011 end-page: 2312 ident: bib0055 article-title: Fault diagnosis of ball bearings using continuous wavelet transform publication-title: Appl Soft Comput – year: 2013 ident: bib0059 article-title: A novel earth mover's distance methodology for image matching with gaussian mixture models publication-title: Proceedings of the IEEE international conference on computer vision – year: 2014 ident: bib0063 article-title: Fast computation of Wasserstein barycenters publication-title: Proceedings of the31st international conference on machine learning, ICML 2014 – year: 2018 ident: bib0003 article-title: Machinery health prognostics: a systematic review from data acquisition to RUL prediction publication-title: Mech Syst Signal Process – volume: 184 start-page: 55 year: 2019 end-page: 66 ident: bib0076 article-title: Reliable multiple combined fault diagnosis of bearings using heterogeneous feature models and multiclass support vector Machines publication-title: Reliab Eng Syst Saf – volume: 191 year: 2019 ident: bib0034 article-title: Reliability analysis of detecting false alarms that employ neural networks: a real case study on wind turbines publication-title: Reliab Eng Syst Saf – year: 2018 ident: bib0089 article-title: Incomplete-data oriented multiview dimension reduction via sparse low-rank representation publication-title: IEEE Trans Neural Netw Learn Syst – volume: 164 start-page: 74 year: 2017 end-page: 83 ident: bib0117 article-title: Remaining useful life prediction of aircraft engine based on degradation pattern learning publication-title: Reliab Eng Syst Saf – year: 2018 ident: bib0110 article-title: Remaining useful life estimation in prognostics using deep convolution neural networks publication-title: Reliab Eng Syst Saf – year: 2016 ident: bib0001 article-title: Some challenges and opportunities in reliability engineering publication-title: IEEE Trans Reliab – year: 2017 ident: bib0058 article-title: Optimal Mass Transport: signal processing and machine-learning applications publication-title: IEEE Signal Process Mag – year: 2020 ident: bib0163 article-title: Utilizing uncertainty information in remaining useful life estimation via Bayesian neural networks and Hamiltonian Monte Carlo publication-title: J Manuf Syst – year: 2020 ident: bib0007 article-title: Prognostics and health management of electronics: fundamentals, machine learning, and the internet of things publication-title: Life Cycle Reliab Saf Eng – volume: 81 start-page: 283 year: 2019 end-page: 299 ident: bib0124 article-title: A novel purity-based k nearest neighbors imputation method and its application in financial distress prediction publication-title: Eng Appl Artif Intell – year: 2003 ident: bib0133 article-title: Developing a prognostic model in the presence of missing data: an ovarian cancer case study publication-title: J Clin Epidemiol – volume: 57 start-page: 5 year: 2016 ident: bib0019 article-title: GE’s big bet on data and analytics publication-title: Proc MIT Sloan Manag Rev – year: 2013 ident: bib0087 article-title: Representation learning: a review and new perspectives publication-title: IEEE Trans Pattern Anal Mach Intell – year: 2016 ident: bib0146 article-title: Sensitivity analysis: a review of recent advances publication-title: Eur J Oper Res – volume: 231 start-page: 350 year: 2017 end-page: 363 ident: bib0159 article-title: A locally adaptive ensemble approach for data-driven prognostics of heterogeneous fleets publication-title: Proc Inst Mech Eng, Part O J Risk Reliab – volume: 7 start-page: 1 year: 2016 end-page: 16 ident: bib0009 article-title: Integrating IVHM and asset design publication-title: Int J Progn Health Manag – year: 2018 ident: bib0011 article-title: Hierarchical modeling of a manufacturing work cell to promote contextualized PHM information across multiple levels publication-title: Manuf Lett – volume: 188 start-page: 432 year: 2019 end-page: 443 ident: bib0085 article-title: Degradation state mining and identification for railway point machines publication-title: Reliab Eng Syst Saf – volume: 55 start-page: 45 year: 2019 end-page: 58 ident: bib0038 article-title: Robust Ada Boost based ensemble of one-class support vector machines publication-title: Inf Fus – volume: 169 start-page: 554 year: 2018 end-page: 569 ident: bib0086 article-title: Data-driven learning framework for associating weather conditions and wind turbine failures publication-title: Reliab Eng Syst Saf – year: 2016 ident: bib0002 article-title: A review of diagnostics and prognostics of low-speed machinery towards wind turbine farm-level health management publication-title: Renew Sustain Energy Rev – year: 2002 ident: bib0145 article-title: On the relative importance of input factors in mathematical models publication-title: J Am Stat Assoc – year: 2011 ident: bib0104 article-title: A review on prognostics and health monitoring of Li-ion battery publication-title: J Power Sources – volume: 92 start-page: 332 year: 2007 end-page: 340 ident: bib0033 article-title: Robust recurrent neural network modeling for software fault detection and correction prediction publication-title: Reliab Eng Syst Saf – year: 2015 ident: bib0152 article-title: Peeking inside the black box: visualizing statistical learning with plots of individual conditional expectation publication-title: J Comput Gr Stat – year: 2009 ident: bib0040 article-title: Multi-class AdaBoost publication-title: Stat Interface – year: 2020 ident: bib0134 article-title: Fault prognostics by an ensemble of Echo State Networks in presence of event based measurements publication-title: Eng Appl Artif Intell – year: 2011 ident: bib0106 article-title: Vibration-based monitoring of civil infrastructure: challenges and successes publication-title: J Civ Struct Health Monit – start-page: 12019. year: 2019 end-page: 8 ident: bib0012 publication-title: A distributed autonomic logistics system with parallel-computing diagnostic algorithm for aircrafts – year: 2011 ident: bib0107 article-title: Remaining useful life estimation - a review on the statistical data driven approaches publication-title: Eur J Oper Res – year: 2009 ident: bib0025 article-title: “Computational methods for reliability and risk analysis,” in – year: 2014 ident: bib0045 article-title: A model-based fault detection and diagnostic methodology based on PCA method and wavelet transform publication-title: Energy Build – volume: 18 start-page: 1 year: 2016 end-page: 43 ident: bib0099 article-title: Using conceptors to manage neural long-term memories for temporal patterns publication-title: Mach Learn – volume: 196 year: 2020 ident: bib0077 article-title: Prediction of pipe failures in water supply networks using logistic regression and support vector classification publication-title: Reliab Eng Syst Saf – year: 2017 ident: bib0046 article-title: Online fault detection methods for chillers combining extended Kalman filter and recursive one-class SVM publication-title: Neurocomputing – volume: 188 start-page: 251 year: 2019 end-page: 262 ident: bib0113 article-title: A new dynamic predictive maintenance framework using deep learning for failure prognostics publication-title: Reliab Eng Syst Saf – year: 2004 ident: bib0026 article-title: Model-based identification of two cracks in a rotor system publication-title: Mech Syst Signal Process – volume: 2 start-page: 84 year: 2011 end-page: 99 ident: bib0103 article-title: A model-based prognostics approach applied to pneumatic valves publication-title: Int J Progn Health Manag – year: 2016 ident: bib0014 article-title: Prediction capability assessment of data-driven prognostic methods for railway applications publication-title: Proceedings of the PHM society european conference PHM society.org – year: 2013 ident: bib0043 article-title: Fault detection in nuclear power plants components by a combination of statistical methods publication-title: IEEE Trans Reliab – volume: 6 year: 2020 ident: bib0155 article-title: Uncertainty in PHM publication-title: Int J Progn Health Manag – reference: . – year: 2020 ident: bib0141 article-title: An Explainable machine learning model for early detection of Parkinson's disease using LIME on DaTSCAN imagery publication-title: Comput. Biol. Med. – year: 2017 ident: bib0005 article-title: Prognostics and health management: a review of vibration based bearing and gear health indicators publication-title: IEEE Access – year: 2018 ident: bib0023 article-title: Detection of weak transient signals based on unsupervised learning for bearing fault diagnosis publication-title: Neurocomputing – year: 2020 ident: bib0097 article-title: Fault diagnostics by conceptors-aided clustering publication-title: 2020 – volume: 34 start-page: 1196 year: 2014 end-page: 1207 ident: bib0156 article-title: Concerns, challenges, and directions of development for the issue of representing uncertainty in risk assessment publication-title: Risk Anal – year: 2017 ident: bib0065 article-title: On wasserstein two-sample testing and related families of nonparametric tests publication-title: Entropy – year: 2016 ident: bib0153 article-title: Practical black-box attacks against Deep learning systems using adversarial examples publication-title: ASIA CCS 2017-Proceedings of the 2017 ACM Asia conference on computer and communications security – year: 1995 ident: bib0136 article-title: Multiple attribute decision making: an introduction – volume: 378 start-page: 686 year: 2019 ident: 10.1016/j.ress.2021.108119_bib0148 article-title: Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations publication-title: J Comput Phys doi: 10.1016/j.jcp.2018.10.045 – year: 2014 ident: 10.1016/j.ress.2021.108119_bib0045 article-title: A model-based fault detection and diagnostic methodology based on PCA method and wavelet transform publication-title: Energy Build – volume: 129 start-page: 1 year: 2014 ident: 10.1016/j.ress.2021.108119_bib0031 article-title: Early detection of gradual concept drifts by text categorization and support Vector Machine techniques: the TRIO algorithm publication-title: Reliab Eng Syst Saf doi: 10.1016/j.ress.2014.03.014 – year: 2016 ident: 10.1016/j.ress.2021.108119_bib0008 article-title: Prognostics: a literature review publication-title: Complex & Intell Syst doi: 10.1007/s40747-016-0019-3 – volume: 11 start-page: 2300 issue: 2 year: 2011 ident: 10.1016/j.ress.2021.108119_bib0055 article-title: Fault diagnosis of ball bearings using continuous wavelet transform publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2010.08.011 – year: 2010 ident: 10.1016/j.ress.2021.108119_bib0126 article-title: An introduction to modern missing data analyses publication-title: J Sch Psychol doi: 10.1016/j.jsp.2009.10.001 – volume: 6 issue: 4 year: 2020 ident: 10.1016/j.ress.2021.108119_bib0155 article-title: Uncertainty in PHM publication-title: Int J Progn Health Manag – year: 2014 ident: 10.1016/j.ress.2021.108119_bib0093 article-title: A review on basic data-driven approaches for industrial process monitoring publication-title: IEEE Trans Ind Electron – year: 2020 ident: 10.1016/j.ress.2021.108119_bib0068 article-title: Fault detection based on optimal transport theory – volume: 58 start-page: 160 year: 2015 ident: 10.1016/j.ress.2021.108119_bib0084 article-title: Clustering for unsupervised fault diagnosis in nuclear turbine shut-down transients publication-title: Mech Syst Signal Process doi: 10.1016/j.ymssp.2014.12.018 – ident: 10.1016/j.ress.2021.108119_bib0147 – volume: 231 start-page: 36 issue: 1 year: 2017 ident: 10.1016/j.ress.2021.108119_bib0015 article-title: A hierarchical decision-making framework for the assessment of the prediction capability of prognostic methods publication-title: J Risk Reliab – year: 2009 ident: 10.1016/j.ress.2021.108119_bib0025 – year: 2016 ident: 10.1016/j.ress.2021.108119_bib0146 article-title: Sensitivity analysis: a review of recent advances publication-title: Eur J Oper Res doi: 10.1016/j.ejor.2015.06.032 – volume: 124 start-page: 479 year: 2019 ident: 10.1016/j.ress.2021.108119_bib0160 article-title: An ensemble of models for integrating dependent sources of information for the prognosis of the remaining useful life of proton exchange membrane fuel cells publication-title: Mech Syst Signal Process doi: 10.1016/j.ymssp.2019.01.060 – year: 2007 ident: 10.1016/j.ress.2021.108119_bib0144 article-title: Ranking bioprocess variables using global sensitivity analysis: a case study in centrifugation publication-title: Bioprocess Biosyst Eng doi: 10.1007/s00449-006-0109-5 – year: 2019 ident: 10.1016/j.ress.2021.108119_bib0092 article-title: Analysis of Spatio-temporal representations for robust footstep recognition with deep residual neural networks publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2018.2799847 – volume: 188 start-page: 251 year: 2019 ident: 10.1016/j.ress.2021.108119_bib0113 article-title: A new dynamic predictive maintenance framework using deep learning for failure prognostics publication-title: Reliab Eng Syst Saf doi: 10.1016/j.ress.2019.03.018 – year: 2010 ident: 10.1016/j.ress.2021.108119_bib0154 article-title: The security of machine learning publication-title: Mach Learn doi: 10.1007/s10994-010-5188-5 – year: 2016 ident: 10.1016/j.ress.2021.108119_bib0014 article-title: Prediction capability assessment of data-driven prognostic methods for railway applications – volume: 186 start-page: 110 year: 2019 ident: 10.1016/j.ress.2021.108119_bib0032 article-title: Robust on-line diagnosis tool for the early accident detection in nuclear power plants publication-title: Reliab Eng Syst Saf doi: 10.1016/j.ress.2019.02.015 – year: 2016 ident: 10.1016/j.ress.2021.108119_bib0061 article-title: Wasserstein training of restricted boltzmann machines publication-title: Adv Neural Inf Process Syst – volume: 188 start-page: 432 year: 2019 ident: 10.1016/j.ress.2021.108119_bib0085 article-title: Degradation state mining and identification for railway point machines publication-title: Reliab Eng Syst Saf doi: 10.1016/j.ress.2019.03.044 – year: 2016 ident: 10.1016/j.ress.2021.108119_bib0017 article-title: IoT-Based prognostics and systems health management for industrial applications publication-title: IEEE Access doi: 10.1109/ACCESS.2016.2587754 – year: 2016 ident: 10.1016/j.ress.2021.108119_bib0153 article-title: Practical black-box attacks against Deep learning systems using adversarial examples – start-page: 703 year: 2019 ident: 10.1016/j.ress.2021.108119_bib0037 article-title: MAD-GAN: Multivariate anomaly detection for time series data with generative adversarial networks – year: 2014 ident: 10.1016/j.ress.2021.108119_bib0063 article-title: Fast computation of Wasserstein barycenters – year: 2018 ident: 10.1016/j.ress.2021.108119_bib0089 article-title: Incomplete-data oriented multiview dimension reduction via sparse low-rank representation publication-title: IEEE Trans Neural Netw Learn Syst doi: 10.1109/TNNLS.2018.2828699 – year: 2011 ident: 10.1016/j.ress.2021.108119_bib0143 article-title: Some considerations on the treatment of uncertainties in risk assessment for practical decision making publication-title: Reliab Eng Syst Saf doi: 10.1016/j.ress.2010.06.001 – year: 2012 ident: 10.1016/j.ress.2021.108119_bib0122 article-title: Missing data: a systematic review of how they are reported and handled publication-title: Epidemiology doi: 10.1097/EDE.0b013e3182576cdb – year: 2017 ident: 10.1016/j.ress.2021.108119_bib0047 article-title: An artificial neural network-based condition monitoring method for wind turbines, with application to the monitoring of the gearbox publication-title: Wind Energy doi: 10.1002/we.2102 – volume: 182 start-page: 208 year: 2019 ident: 10.1016/j.ress.2021.108119_bib0109 article-title: Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction publication-title: Reliab Eng Syst Saf doi: 10.1016/j.ress.2018.11.011 – year: 2001 ident: 10.1016/j.ress.2021.108119_bib0151 article-title: Greedy function approximation: a gradient boosting machine publication-title: Ann Stat doi: 10.1214/aos/1013203451 – volume: 193 year: 2020 ident: 10.1016/j.ress.2021.108119_bib0028 article-title: A two-level Bayesian early fault detection for mechanical equipment subject to dependent failure modes publication-title: Reliab Eng Syst Saf doi: 10.1016/j.ress.2019.106676 – year: 2015 ident: 10.1016/j.ress.2021.108119_bib0044 article-title: Robust signal reconstruction for condition monitoring of industrial components via a modified auto associative kernel regression method publication-title: Mech Syst Signal Process doi: 10.1016/j.ymssp.2014.09.013 – year: 2012 ident: 10.1016/j.ress.2021.108119_bib0070 article-title: Distributed soft fault detection algorithm in wireless sensor networks using statistical test – volume: 63 start-page: 3137 issue: 5 year: 2016 ident: 10.1016/j.ress.2021.108119_bib0095 article-title: An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data publication-title: IEEE Trans Ind Electron doi: 10.1109/TIE.2016.2519325 – year: 2018 ident: 10.1016/j.ress.2021.108119_bib0011 article-title: Hierarchical modeling of a manufacturing work cell to promote contextualized PHM information across multiple levels publication-title: Manuf Lett doi: 10.1016/j.mfglet.2018.02.003 – year: 2011 ident: 10.1016/j.ress.2021.108119_bib0050 article-title: Q-statistic and t2-statistic pca-based measures for damage assessment in structures publication-title: Struct Health Monit doi: 10.1177/1475921710388972 – year: 2020 ident: 10.1016/j.ress.2021.108119_bib0007 article-title: Prognostics and health management of electronics: fundamentals, machine learning, and the internet of things publication-title: Life Cycle Reliab Saf Eng doi: 10.1007/s41872-020-00119-y – year: 2018 ident: 10.1016/j.ress.2021.108119_bib0003 article-title: Machinery health prognostics: a systematic review from data acquisition to RUL prediction publication-title: Mech Syst Signal Process – year: 2018 ident: 10.1016/j.ress.2021.108119_bib0054 article-title: Study on nonlinear dynamic characteristics inherent in offshore jacket platform using long-term monitored response of ice-structure interaction publication-title: Appl Ocean Res doi: 10.1016/j.apor.2017.12.009 – volume: 67 start-page: 2283 issue: 3 year: 2019 ident: 10.1016/j.ress.2021.108119_bib0162 article-title: Bayesian deep-learning-based health prognostics toward prognostics uncertainty publication-title: IEEE Trans Ind Electron doi: 10.1109/TIE.2019.2907440 – year: 2017 ident: 10.1016/j.ress.2021.108119_bib0058 article-title: Optimal Mass Transport: signal processing and machine-learning applications publication-title: IEEE Signal Process Mag doi: 10.1109/MSP.2017.2695801 – ident: 10.1016/j.ress.2021.108119_bib0149 – year: 2019 ident: 10.1016/j.ress.2021.108119_bib0039 article-title: Anomaly detection for industrial systems using generative adversarial networks – volume: 183 start-page: 240 year: 2019 ident: 10.1016/j.ress.2021.108119_bib0112 article-title: Remaining useful life predictions for turbofan engine degradation using semi-supervised deep architecture publication-title: Reliab Eng Syst Saf doi: 10.1016/j.ress.2018.11.027 – year: 2013 ident: 10.1016/j.ress.2021.108119_bib0087 article-title: Representation learning: a review and new perspectives publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2013.50 – year: 2017 ident: 10.1016/j.ress.2021.108119_bib0065 article-title: On wasserstein two-sample testing and related families of nonparametric tests publication-title: Entropy doi: 10.3390/e19020047 – volume: 34 start-page: 1196 issue: 7 year: 2014 ident: 10.1016/j.ress.2021.108119_bib0156 article-title: Concerns, challenges, and directions of development for the issue of representing uncertainty in risk assessment publication-title: Risk Anal doi: 10.1111/risa.12247 – year: 2020 ident: 10.1016/j.ress.2021.108119_bib0138 article-title: A novel deep learning method based on attention mechanism for bearing remaining useful life prediction publication-title: Applied Soft Computing Journal – volume: 92 start-page: 332 issue: 3 year: 2007 ident: 10.1016/j.ress.2021.108119_bib0033 article-title: Robust recurrent neural network modeling for software fault detection and correction prediction publication-title: Reliab Eng Syst Saf doi: 10.1016/j.ress.2006.04.007 – volume: 96 start-page: 480 issue: 4 year: 2011 ident: 10.1016/j.ress.2021.108119_bib0157 article-title: Classifier-ensemble incremental-learning procedure for nuclear transient identification at different operational conditions publication-title: Reliab Eng Syst Saf doi: 10.1016/j.ress.2010.11.005 – year: 2003 ident: 10.1016/j.ress.2021.108119_bib0133 article-title: Developing a prognostic model in the presence of missing data: an ovarian cancer case study publication-title: J Clin Epidemiol doi: 10.1016/S0895-4356(02)00539-5 – volume: 180 start-page: 385 year: 2018 ident: 10.1016/j.ress.2021.108119_bib0111 article-title: Developing ladder network for intelligent evaluation system: case of remaining useful life prediction for centrifugal pumps publication-title: Reliability Eng Syst Saf doi: 10.1016/j.ress.2018.08.010 – year: 2011 ident: 10.1016/j.ress.2021.108119_bib0106 article-title: Vibration-based monitoring of civil infrastructure: challenges and successes publication-title: J Civ Struct Health Monit doi: 10.1007/s13349-011-0009-5 – ident: 10.1016/j.ress.2021.108119_bib0164 – year: 2010 ident: 10.1016/j.ress.2021.108119_bib0132 article-title: Comparison of techniques for handling missing covariate data within prognostic modelling studies: a simulation study publication-title: BMC Med Res Methodol doi: 10.1186/1471-2288-10-7 – year: 2013 ident: 10.1016/j.ress.2021.108119_bib0056 article-title: Early classification of bearing faults using morphological operators and fuzzy inference publication-title: IEEE Trans Ind Electron doi: 10.1109/TIE.2012.2188259 – volume: 54 start-page: 561 issue: 2 year: 2010 ident: 10.1016/j.ress.2021.108119_bib0129 article-title: What to do about missing values in time-series cross-section data publication-title: Am J Pol Sci doi: 10.1111/j.1540-5907.2010.00447.x – year: 2019 ident: 10.1016/j.ress.2021.108119_bib0125 article-title: An integrated imputation-prediction scheme for prognostics of battery data with missing observations publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2018.08.033 – volume: 121 start-page: 198 year: 2014 ident: 10.1016/j.ress.2021.108119_bib0072 article-title: Predicting component reliability and level of degradation with complex-valued neural networks publication-title: Reliab Eng Syst Saf doi: 10.1016/j.ress.2013.08.004 – year: 2015 ident: 10.1016/j.ress.2021.108119_bib0152 article-title: Peeking inside the black box: visualizing statistical learning with plots of individual conditional expectation publication-title: J Comput Gr Stat doi: 10.1080/10618600.2014.907095 – volume: 231 start-page: 350 issue: 4 year: 2017 ident: 10.1016/j.ress.2021.108119_bib0159 article-title: A locally adaptive ensemble approach for data-driven prognostics of heterogeneous fleets publication-title: Proc Inst Mech Eng, Part O J Risk Reliab – volume: 2 start-page: 84 issue: 2 year: 2011 ident: 10.1016/j.ress.2021.108119_bib0103 article-title: A model-based prognostics approach applied to pneumatic valves publication-title: Int J Progn Health Manag – volume: 185 start-page: 502 year: 2019 ident: 10.1016/j.ress.2021.108119_bib0108 article-title: Remaining useful life prediction based on health index similarity publication-title: Reliab Eng Syst Saf doi: 10.1016/j.ress.2019.02.002 – year: 2019 ident: 10.1016/j.ress.2021.108119_bib0096 article-title: An adaptive spatiotemporal feature learning approach for fault diagnosis in complex systems publication-title: Mech Syst Signal Process doi: 10.1016/j.ymssp.2018.07.048 – year: 2013 ident: 10.1016/j.ress.2021.108119_bib0043 article-title: Fault detection in nuclear power plants components by a combination of statistical methods publication-title: IEEE Trans Reliab doi: 10.1109/TR.2013.2285033 – year: 2009 ident: 10.1016/j.ress.2021.108119_bib0071 article-title: Fault detection using principal component Analysis (PCA) in a wastewater treatment plant (WWTP) – volume: 169 start-page: 554 year: 2018 ident: 10.1016/j.ress.2021.108119_bib0086 article-title: Data-driven learning framework for associating weather conditions and wind turbine failures publication-title: Reliab Eng Syst Saf doi: 10.1016/j.ress.2017.10.004 – year: 2008 ident: 10.1016/j.ress.2021.108119_bib0135 article-title: Self-adaptive differential evolution with neighborhood search – year: 2020 ident: 10.1016/j.ress.2021.108119_bib0006 article-title: Challenges to IoT-enabled predictive maintenance for industry 4.0 publication-title: IEEE Internet Things J doi: 10.1109/JIOT.2019.2957029 – volume: 57 start-page: 5 year: 2016 ident: 10.1016/j.ress.2021.108119_bib0019 article-title: GE’s big bet on data and analytics publication-title: Proc MIT Sloan Manag Rev – volume: 196 year: 2020 ident: 10.1016/j.ress.2021.108119_bib0077 article-title: Prediction of pipe failures in water supply networks using logistic regression and support vector classification publication-title: Reliab Eng Syst Saf doi: 10.1016/j.ress.2019.106754 – volume: 17 start-page: 317 issue: 2 year: 2003 ident: 10.1016/j.ress.2021.108119_bib0101 article-title: Artificial neural network based fault diagnostics of rolling element bearings using time-domain features publication-title: Mech Syst Signal Process doi: 10.1006/mssp.2001.1462 – year: 2017 ident: 10.1016/j.ress.2021.108119_bib0004 article-title: Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: a review publication-title: Appl Energy doi: 10.1016/j.apenergy.2017.04.048 – volume: 115 start-page: 124 year: 2013 ident: 10.1016/j.ress.2021.108119_bib0073 article-title: Failure diagnosis using deep belief learning based health state classification publication-title: Reliab Eng Syst Saf doi: 10.1016/j.ress.2013.02.022 – year: 2002 ident: 10.1016/j.ress.2021.108119_bib0145 article-title: On the relative importance of input factors in mathematical models publication-title: J Am Stat Assoc doi: 10.1198/016214502388618447 – volume: 138 start-page: 219 year: 2015 ident: 10.1016/j.ress.2021.108119_bib0116 article-title: Hybrid PSO-SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability publication-title: Reliab Eng Syst Saf doi: 10.1016/j.ress.2015.02.001 – volume: 47 start-page: 553 year: 2016 ident: 10.1016/j.ress.2021.108119_bib0158 article-title: A SVR-based ensemble approach for drifting data streams with recurring patterns publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2016.06.030 – year: 2019 ident: 10.1016/j.ress.2021.108119_bib0021 article-title: Unsupervised deep learning of compact binary descriptors publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2018.2833865 – year: 2015 ident: 10.1016/j.ress.2021.108119_bib0079 article-title: Unsupervised clustering of vibration signals for identifying anomalous conditions in a nuclear turbine publication-title: J Intell Fuzzy Syst – year: 2020 ident: 10.1016/j.ress.2021.108119_bib0163 article-title: Utilizing uncertainty information in remaining useful life estimation via Bayesian neural networks and Hamiltonian Monte Carlo publication-title: J Manuf Syst – year: 2017 ident: 10.1016/j.ress.2021.108119_bib0005 article-title: Prognostics and health management: a review of vibration based bearing and gear health indicators publication-title: IEEE Access – year: 1995 ident: 10.1016/j.ress.2021.108119_bib0136 – year: 2004 ident: 10.1016/j.ress.2021.108119_bib0026 article-title: Model-based identification of two cracks in a rotor system publication-title: Mech Syst Signal Process doi: 10.1016/S0888-3270(03)00041-4 – volume: 184 start-page: 27 year: 2019 ident: 10.1016/j.ress.2021.108119_bib0027 article-title: Hyungcheol Min, “Fault detection and identification method using observer-based residuals publication-title: Reliab Eng System Saf doi: 10.1016/j.ress.2018.02.007 – year: 2017 ident: 10.1016/j.ress.2021.108119_bib0046 article-title: Online fault detection methods for chillers combining extended Kalman filter and recursive one-class SVM publication-title: Neurocomputing doi: 10.1016/j.neucom.2016.09.076 – volume: 20 start-page: 1483 issue: 7 year: 2006 ident: 10.1016/j.ress.2021.108119_bib0053 article-title: A review on machinery diagnostics and prognostics implementing condition-based maintenance publication-title: Mech Syst Signal Process doi: 10.1016/j.ymssp.2005.09.012 – year: 2016 ident: 10.1016/j.ress.2021.108119_bib0088 article-title: An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big Data publication-title: IEEE Trans Ind Electron – year: 2020 ident: 10.1016/j.ress.2021.108119_bib0013 article-title: Designing mission abort strategies based on early-warning information: application to UAV publication-title: IEEE Trans Ind Inf – year: 2006 ident: 10.1016/j.ress.2021.108119_bib0052 article-title: A review and comparison of methods for detecting outliersin univariate data sets publication-title: Dep Biostat Grad Sch Public Health – year: 2018 ident: 10.1016/j.ress.2021.108119_bib0080 article-title: Motion segmentation via generalized curvatures publication-title: IEEE Trans Pattern Anal Mach Intell – year: 2020 ident: 10.1016/j.ress.2021.108119_bib0139 article-title: Intelligent prognostics of machining tools based on adaptive variational mode decomposition and deep learning method with attention mechanism publication-title: Neurocomputing – year: 2009 ident: 10.1016/j.ress.2021.108119_bib0041 article-title: Data-driven fault detection and diagnosis for complex industrial processes – year: 2016 ident: 10.1016/j.ress.2021.108119_bib0020 article-title: Edge computing: vision and challenges publication-title: IEEE Int Things J – volume: 196 year: 2020 ident: 10.1016/j.ress.2021.108119_bib0161 article-title: Controlling the accuracy and uncertainty trade-off in RUL prediction with a surrogate wiener propagation model publication-title: Reliab Eng Syst Saf doi: 10.1016/j.ress.2019.106727 – year: 2019 ident: 10.1016/j.ress.2021.108119_bib0029 article-title: Bayesian multi-task learning methodology for reconstruction of structural health monitoring data publication-title: Struct Health Monit doi: 10.1177/1475921718794953 – year: 2019 ident: 10.1016/j.ress.2021.108119_bib0090 article-title: Deep latent low-rank representation for face sketch synthesis publication-title: IEEE Trans Neural Netw Learn Syst – year: 2018 ident: 10.1016/j.ress.2021.108119_bib0069 article-title: The cumulative distribution transform and linear pattern classification publication-title: Appl Comput Harmon Anal doi: 10.1016/j.acha.2017.02.002 – volume: 186 start-page: 51 year: 2019 ident: 10.1016/j.ress.2021.108119_bib0120 article-title: A hybrid prognostic method for system degradation based on particle filter and relevance vector machine publication-title: Reliab Eng Syst Saf doi: 10.1016/j.ress.2019.02.011 – year: 2020 ident: 10.1016/j.ress.2021.108119_bib0134 article-title: Fault prognostics by an ensemble of Echo State Networks in presence of event based measurements publication-title: Eng Appl Artif Intell doi: 10.1016/j.engappai.2019.103346 – year: 2019 ident: 10.1016/j.ress.2021.108119_bib0091 article-title: Deep supervision with intermediate concepts publication-title: IEEE Trans Pattern Anal Mach Intell – year: 2017 ident: 10.1016/j.ress.2021.108119_bib0078 article-title: Industrial big data for fault diagnosis: taxonomy, review, and applications publication-title: IEEE Access – volume: 184 start-page: 2 year: 2019 ident: 10.1016/j.ress.2021.108119_bib0075 article-title: Dirt and mud detection and diagnosis on a wind turbine blade employing guided waves and supervised learning classifiers publication-title: Reliab Eng Syst Saf doi: 10.1016/j.ress.2018.02.013 – year: 2017 ident: 10.1016/j.ress.2021.108119_bib0018 article-title: A machine learning decision-support system improves the internet of things’ smart meter operations publication-title: IEEE Int Things J – year: 2010 ident: 10.1016/j.ress.2021.108119_bib0128 article-title: The search for stable prognostic models in multiple imputed data sets publication-title: BMC Med Res Methodol doi: 10.1186/1471-2288-10-81 – volume: 186 start-page: 24 year: 2019 ident: 10.1016/j.ress.2021.108119_bib0074 article-title: Comparison of automatic and guided learning for Bayesian networks to analyse pipe failures in the water distribution system publication-title: Reliab Eng Syst Saf doi: 10.1016/j.ress.2019.02.001 – volume: 191 year: 2019 ident: 10.1016/j.ress.2021.108119_bib0034 article-title: Reliability analysis of detecting false alarms that employ neural networks: a real case study on wind turbines publication-title: Reliab Eng Syst Saf – year: 2011 ident: 10.1016/j.ress.2021.108119_bib0104 article-title: A review on prognostics and health monitoring of Li-ion battery publication-title: J Power Sources – volume: 70 start-page: 1034 year: 2018 ident: 10.1016/j.ress.2021.108119_bib0100 article-title: A simple feedforward convolutional conceptor neural network for classification publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2017.08.016 – issue: 107614 year: 2021 ident: 10.1016/j.ress.2021.108119_bib0030 article-title: A fault detector/classifier for closed-ring power generators using machine learning publication-title: Reliab Eng Syst Saf – year: 2011 ident: 10.1016/j.ress.2021.108119_bib0107 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 – year: 2016 ident: 10.1016/j.ress.2021.108119_bib0140 article-title: ‘Why should i trust you?’ Explaining the predictions of any classifier – volume: 1 start-page: 4 issue: 1 year: 2010 ident: 10.1016/j.ress.2021.108119_bib0016 article-title: Metrics for offline evaluation of prognostic performance publication-title: Int J Progn Health Manag – year: 2020 ident: 10.1016/j.ress.2021.108119_bib0097 article-title: Fault diagnostics by conceptors-aided clustering – year: 2016 ident: 10.1016/j.ress.2021.108119_bib0001 article-title: Some challenges and opportunities in reliability engineering publication-title: IEEE Trans Reliab doi: 10.1109/TR.2016.2591504 – year: 2015 ident: 10.1016/j.ress.2021.108119_bib0102 article-title: Uncertainty in prognostics and systems health management publication-title: Int J Progn Health Manag – year: 2006 ident: 10.1016/j.ress.2021.108119_bib0127 article-title: Review: a gentle introduction to imputation of missing values publication-title: J Clin Epidemiol doi: 10.1016/j.jclinepi.2006.01.014 – year: 2019 ident: 10.1016/j.ress.2021.108119_bib0081 article-title: Hashing with mutual information publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2019.2914897 – year: 2017 ident: 10.1016/j.ress.2021.108119_bib0051 article-title: Sensor fault diagnosis for structural health monitoring based on statistical hypothesis test and missing variable approach publication-title: J Aerosp Eng doi: 10.1061/(ASCE)AS.1943-5525.0000572 – year: 2017 ident: 10.1016/j.ress.2021.108119_bib0057 article-title: A review of feature extraction methods in vibration-based condition monitoring and its application for degradation trend estimation of low-speed slew bearing publication-title: Machines doi: 10.3390/machines5040021 – volume: 205 year: 2021 ident: 10.1016/j.ress.2021.108119_bib0114 article-title: A dual-LSTM framework combining change point detection and remaining useful life prediction publication-title: Reliab Eng Syst Saf doi: 10.1016/j.ress.2020.107257 – ident: 10.1016/j.ress.2021.108119_bib0150 doi: 10.1109/TKDE.2021.3079836 – volume: 184 start-page: 55 year: 2019 ident: 10.1016/j.ress.2021.108119_bib0076 article-title: Reliable multiple combined fault diagnosis of bearings using heterogeneous feature models and multiclass support vector Machines publication-title: Reliab Eng Syst Saf doi: 10.1016/j.ress.2018.02.012 – year: 2014 ident: 10.1016/j.ress.2021.108119_bib0060 article-title: Adaptive color transfer with relaxed optimal transport – start-page: 12019. year: 2019 ident: 10.1016/j.ress.2021.108119_bib0012 – volume: 55 start-page: 45 issue: August 2019 year: 2019 ident: 10.1016/j.ress.2021.108119_bib0038 article-title: Robust Ada Boost based ensemble of one-class support vector machines publication-title: Inf Fus – volume: 185 start-page: 372 year: 2019 ident: 10.1016/j.ress.2021.108119_bib0115 article-title: Gated recurrent unit based recurrent neural network for remaining useful life prediction of nonlinear deterioration process publication-title: Reliab Eng Syst Saf doi: 10.1016/j.ress.2019.01.006 – volume: 160 start-page: 21 year: 2017 ident: 10.1016/j.ress.2021.108119_bib0105 article-title: System dynamic reliability assessment and failure prognostics publication-title: Reliab Eng Syst Saf doi: 10.1016/j.ress.2016.12.003 – year: 2017 ident: 10.1016/j.ress.2021.108119_bib0131 article-title: Architecture for hybrid modelling and its application to diagnosis and prognosis with missing data publication-title: Meas J Int Meas Confed doi: 10.1016/j.measurement.2017.02.003 – year: 2020 ident: 10.1016/j.ress.2021.108119_bib0141 article-title: An Explainable machine learning model for early detection of Parkinson's disease using LIME on DaTSCAN imagery publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2020.104041 – year: 2009 ident: 10.1016/j.ress.2021.108119_bib0064 article-title: Fast and robust earth mover's distances – year: 2018 ident: 10.1016/j.ress.2021.108119_bib0023 article-title: Detection of weak transient signals based on unsupervised learning for bearing fault diagnosis publication-title: Neurocomputing doi: 10.1016/j.neucom.2018.07.004 – volume: 182 start-page: 1 year: 2019 ident: 10.1016/j.ress.2021.108119_bib0121 article-title: Physics-based prognostics of lithium-ion battery using non-linear least squares with dynamic bounds publication-title: Reliab Eng Syst Saf doi: 10.1016/j.ress.2018.09.018 – year: 2012 ident: 10.1016/j.ress.2021.108119_bib0062 article-title: A kernel two-sample test publication-title: J Mach Learn Res – start-page: 4768 year: 2017 ident: 10.1016/j.ress.2021.108119_bib0137 article-title: A unified approach to interpreting model predictions – year: 2017 ident: 10.1016/j.ress.2021.108119_bib0130 article-title: Fault diagnosis and prognosis of wastewater processes with incomplete data by the auto-associative neural networks and ARMA model publication-title: Chemom Intell Lab Syst doi: 10.1016/j.chemolab.2016.12.009 – year: 2018 ident: 10.1016/j.ress.2021.108119_bib0042 article-title: A novel hybrid of auto-associative kernel regression and dynamic independent component analysis for fault detection in nonlinear multimode processes publication-title: J Process Control doi: 10.1016/j.jprocont.2018.05.004 – year: 2015 ident: 10.1016/j.ress.2021.108119_bib0066 article-title: Distance-based analysis of dynamical systems reconstructed from vibrations for bearing diagnostics publication-title: Nonlinear Dyn doi: 10.1007/s11071-014-1857-4 – year: 2013 ident: 10.1016/j.ress.2021.108119_bib0059 article-title: A novel earth mover's distance methodology for image matching with gaussian mixture models – volume: 184 start-page: 228 year: 2019 ident: 10.1016/j.ress.2021.108119_bib0118 article-title: Remaining useful life estimation in aeronautics: combining data-driven and Kalman filtering publication-title: Reliab Eng Syst Saf doi: 10.1016/j.ress.2018.01.017 – year: 2016 ident: 10.1016/j.ress.2021.108119_bib0010 article-title: An approach to designing PHM systems with systems engineering – year: 2018 ident: 10.1016/j.ress.2021.108119_bib0094 article-title: Fault diagnosis approach for photovoltaic arrays based on unsupervised sample clustering and probabilistic neural network model publication-title: Sol Energy doi: 10.1016/j.solener.2018.10.054 – volume: 3 start-page: 127 issue: 3 year: 2009 ident: 10.1016/j.ress.2021.108119_bib0098 article-title: Reservoir computing approaches to recurrent neural network training publication-title: Comput Sci Rev doi: 10.1016/j.cosrev.2009.03.005 – volume: 87 start-page: 401 issue: October 2016 year: 2017 ident: 10.1016/j.ress.2021.108119_bib0035 article-title: A SVM framework for fault detection of the braking system in a high speed train publication-title: Mech Syst Signal Process doi: 10.1016/j.ymssp.2016.10.034 – year: 2016 ident: 10.1016/j.ress.2021.108119_bib0036 article-title: A survey of network anomaly detection techniques publication-title: J Netw Comput Appl doi: 10.1016/j.jnca.2015.11.016 – year: 2014 ident: 10.1016/j.ress.2021.108119_bib0142 article-title: Explaining prediction models and individual predictions with feature contributions publication-title: Knowl Inf Syst doi: 10.1007/s10115-013-0679-x – volume: 81 start-page: 283 year: 2019 ident: 10.1016/j.ress.2021.108119_bib0124 article-title: A novel purity-based k nearest neighbors imputation method and its application in financial distress prediction publication-title: Eng Appl Artif Intell doi: 10.1016/j.engappai.2019.03.003 – volume: 164 start-page: 74 year: 2017 ident: 10.1016/j.ress.2021.108119_bib0117 article-title: Remaining useful life prediction of aircraft engine based on degradation pattern learning publication-title: Reliab Eng Syst Saf doi: 10.1016/j.ress.2017.02.007 – year: 2019 ident: 10.1016/j.ress.2021.108119_bib0049 article-title: Real-time condition monitoring and fault detection of components based on machine-learning reconstruction model publication-title: Renew Energy doi: 10.1016/j.renene.2018.10.062 – year: 2016 ident: 10.1016/j.ress.2021.108119_bib0002 article-title: A review of diagnostics and prognostics of low-speed machinery towards wind turbine farm-level health management publication-title: Renew Sustain Energy Rev doi: 10.1016/j.rser.2015.08.061 – volume: 18 start-page: 1 year: 2016 ident: 10.1016/j.ress.2021.108119_bib0099 article-title: Using conceptors to manage neural long-term memories for temporal patterns publication-title: Mach Learn – year: 2019 ident: 10.1016/j.ress.2021.108119_bib0067 article-title: Change point and fault detection using Kantorovich distance publication-title: J Process Control doi: 10.1016/j.jprocont.2019.05.012 – volume: 7 start-page: 1 year: 2016 ident: 10.1016/j.ress.2021.108119_bib0009 article-title: Integrating IVHM and asset design publication-title: Int J Progn Health Manag – year: 2009 ident: 10.1016/j.ress.2021.108119_bib0040 article-title: Multi-class AdaBoost publication-title: Stat Interface doi: 10.4310/SII.2009.v2.n3.a8 – year: 2015 ident: 10.1016/j.ress.2021.108119_bib0123 article-title: An imputation-based matrix factorization method for improving accuracy of collaborative filtering systems publication-title: Eng Appl Artif Intell doi: 10.1016/j.engappai.2015.08.010 – year: 2017 ident: 10.1016/j.ress.2021.108119_bib0024 – year: 2008 ident: 10.1016/j.ress.2021.108119_bib0048 article-title: Observer and data-driven-model-based fault detection in power plant coal mills publication-title: IEEE Trans Energy Convers doi: 10.1109/TEC.2007.914185 – year: 2018 ident: 10.1016/j.ress.2021.108119_bib0082 article-title: Sparse Temporal encoding of visual features for robust object recognition by spiking neurons publication-title: IEEE Trans Neural Netw Learn Syst doi: 10.1109/TNNLS.2018.2812811 – volume: 184 start-page: 67 year: 2019 ident: 10.1016/j.ress.2021.108119_bib0119 article-title: A reliable technique for remaining useful life estimation of rolling element bearings using dynamic regression models publication-title: Reliab Eng Syst Saf doi: 10.1016/j.ress.2018.02.003 – year: 2018 ident: 10.1016/j.ress.2021.108119_bib0022 article-title: Unsupervised fault diagnosis of rolling bearings using a deep neural network based on generative adversarial networks publication-title: Neurocomputing – year: 2018 ident: 10.1016/j.ress.2021.108119_bib0110 article-title: Remaining useful life estimation in prognostics using deep convolution neural networks publication-title: Reliab Eng Syst Saf doi: 10.1201/9781315273860 – year: 2019 ident: 10.1016/j.ress.2021.108119_bib0083 article-title: Feedback convolutional neural network for visual localization and segmentation publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2018.2843329 |
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| SubjectTerms | Cyber-physical systems Deep Neural Networks (DNNs) Engineering Sciences Generative Adversarial Networks (GANs) Optimal Transport (OT) Predictive maintenance Prognostics and Health Management (PHM) Recurrent Neural Networks (RNNs), Reservoir Computing (RC) Reliability engineering |
| Title | Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice |
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