Dynamic Routing-based Multimodal Neural Network for Multi-sensory Fault Diagnosis of Induction Motor
•Dynamic routing-based multimodal neural network for multi-sensory data fusion.•>Multimodal feature extraction schema is designed to enhance the diagnostic performance.•>Effectiveness is experimentally validated with induction motor fault dataset. Induction motor is the main drive power in mod...
Saved in:
| Published in: | Journal of manufacturing systems Vol. 55; pp. 264 - 272 |
|---|---|
| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier Ltd
01.04.2020
|
| Subjects: | |
| ISSN: | 0278-6125, 1878-6642 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | •Dynamic routing-based multimodal neural network for multi-sensory data fusion.•>Multimodal feature extraction schema is designed to enhance the diagnostic performance.•>Effectiveness is experimentally validated with induction motor fault dataset.
Induction motor is the main drive power in modern manufacturing, and timely fault diagnosis of induction motor is of significance to production safety, part quality and maintenance cost control. Data fusion-based diagnosis is attractive for effective utilization of multi-source monitoring information of motors with the development of industrial internet of things. A new multi-sensory fusion model is proposed, named dynamic routing-based multimodal neural network (DRMNN), following the paradigm of multimodal deep learning (MDL). Specifically, the fusion of vibration and stator current signals are investigated. A multimodal feature extraction scheme is designed for dimensionality reduction and invariant features capturing based on multi-source information. Since it is necessary to determine the importance of each modality, a dynamic routing algorithm is introduced in the decision layer to adaptively assign proper weights to different modalities. The effectiveness and robustness of developed DRMNN is demonstrated in the experimental studies performed on a motor test rig. In comparison with similar neural networks without data fusion and other state-of-art fusion techniques, the proposed DRMNN yields better performance. |
|---|---|
| AbstractList | •Dynamic routing-based multimodal neural network for multi-sensory data fusion.•>Multimodal feature extraction schema is designed to enhance the diagnostic performance.•>Effectiveness is experimentally validated with induction motor fault dataset.
Induction motor is the main drive power in modern manufacturing, and timely fault diagnosis of induction motor is of significance to production safety, part quality and maintenance cost control. Data fusion-based diagnosis is attractive for effective utilization of multi-source monitoring information of motors with the development of industrial internet of things. A new multi-sensory fusion model is proposed, named dynamic routing-based multimodal neural network (DRMNN), following the paradigm of multimodal deep learning (MDL). Specifically, the fusion of vibration and stator current signals are investigated. A multimodal feature extraction scheme is designed for dimensionality reduction and invariant features capturing based on multi-source information. Since it is necessary to determine the importance of each modality, a dynamic routing algorithm is introduced in the decision layer to adaptively assign proper weights to different modalities. The effectiveness and robustness of developed DRMNN is demonstrated in the experimental studies performed on a motor test rig. In comparison with similar neural networks without data fusion and other state-of-art fusion techniques, the proposed DRMNN yields better performance. |
| Author | Gao, Robert X. Zhang, Xing Zhang, Laibin Fu, Peilun Wang, Jinjiang |
| Author_xml | – sequence: 1 givenname: Peilun surname: Fu fullname: Fu, Peilun organization: School of Safety and Ocean Engineering, China University of Petroleum, Beijing 102249, China – sequence: 2 givenname: Jinjiang surname: Wang fullname: Wang, Jinjiang email: jwang@cup.edu.cn organization: School of Safety and Ocean Engineering, China University of Petroleum, Beijing 102249, China – sequence: 3 givenname: Xing surname: Zhang fullname: Zhang, Xing organization: PetroChina Pipeline Research & Development Center, Langfang, Hebei 065099, China – sequence: 4 givenname: Laibin surname: Zhang fullname: Zhang, Laibin organization: School of Safety and Ocean Engineering, China University of Petroleum, Beijing 102249, China – sequence: 5 givenname: Robert X. surname: Gao fullname: Gao, Robert X. organization: Department of Mechanical & Aerospace Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA |
| BookMark | eNp9kM9KAzEQh4NUsK2-gKe8wK5J9j94kdZqwSqInsNski1ZdxNJUmXf3l3ryUNPM8PwDfP7FmhmrFEIXVMSU0LzmzZuez_EjDASkzQmpDpDc1oWZZTnKZuhOWFTT1l2gRbet4RQlhI2R3I9GOi1wK_2ELTZRzV4JfHu0AXdWwkdflYH91vCt3UfuLHuuI28Mt66AW9gHPFaw95Yrz22Dd4aeRBBW4N3Nlh3ic4b6Ly6-qtL9L65f1s9Rk8vD9vV3VMkEkJCRGXFGgWyaFKV5QTqPK8AMlpmVUElqAxkXieZKCpRFapMC1bnkgIkiazGQJAsETveFc5671TDP53uwQ2cEj554i2fPPHJEycpHz2NUPkPEjrA9HxwoLvT6O0RVWOoL60c90IrI5TUTonApdWn8B-P5og_ |
| CitedBy_id | crossref_primary_10_1016_j_inffus_2024_102453 crossref_primary_10_1016_j_iswa_2022_200167 crossref_primary_10_1016_j_inffus_2023_102134 crossref_primary_10_1016_j_jmapro_2022_08_036 crossref_primary_10_3390_s22166075 crossref_primary_10_3390_s23052649 crossref_primary_10_1109_ACCESS_2023_3307770 crossref_primary_10_1109_TTE_2024_3502466 crossref_primary_10_1016_j_measurement_2021_109494 crossref_primary_10_1109_TASE_2022_3141248 crossref_primary_10_1016_j_engappai_2025_110663 crossref_primary_10_1007_s42835_022_01004_7 crossref_primary_10_1109_TII_2023_3248110 crossref_primary_10_1080_08839514_2022_2055396 crossref_primary_10_1016_j_jmsy_2020_07_003 crossref_primary_10_1155_2023_6271241 crossref_primary_10_3390_machines12070495 crossref_primary_10_1016_j_ress_2021_108018 crossref_primary_10_1016_j_jmsy_2021_03_022 crossref_primary_10_1109_ACCESS_2024_3508030 crossref_primary_10_1155_2022_5170734 crossref_primary_10_1109_JSEN_2024_3384516 crossref_primary_10_1109_ACCESS_2024_3508271 crossref_primary_10_1016_j_iswa_2022_200112 crossref_primary_10_1109_ACCESS_2024_3434635 crossref_primary_10_1016_j_eswa_2025_127726 crossref_primary_10_1007_s12541_023_00947_9 crossref_primary_10_1007_s40684_025_00712_5 crossref_primary_10_1016_j_jmsy_2022_02_004 crossref_primary_10_1016_j_jmsy_2023_11_020 crossref_primary_10_1177_01423312231157118 crossref_primary_10_1016_j_measurement_2024_114617 crossref_primary_10_1016_j_jlp_2022_104740 crossref_primary_10_1109_TMECH_2022_3169143 crossref_primary_10_1016_j_measen_2023_100944 crossref_primary_10_1016_j_suscom_2022_100763 crossref_primary_10_1016_j_jmsy_2020_09_001 crossref_primary_10_3390_s25010092 crossref_primary_10_1016_j_jmsy_2020_10_007 crossref_primary_10_1371_journal_pone_0256287 crossref_primary_10_1016_j_ress_2023_109676 crossref_primary_10_1088_1361_6501_ad6e14 crossref_primary_10_1109_TNNLS_2023_3247163 crossref_primary_10_1109_MIE_2023_3265505 crossref_primary_10_1016_j_jmsy_2020_08_010 crossref_primary_10_1007_s00202_024_02420_w crossref_primary_10_1016_j_engappai_2025_111767 |
| Cites_doi | 10.1109/TII.2018.2793246 10.1016/j.ymssp.2018.02.009 10.1109/TMECH.2017.2759791 10.1016/j.ymssp.2013.06.001 10.1016/j.jmsy.2018.01.003 10.1016/j.jmsy.2016.01.003 10.1016/j.ymssp.2016.06.032 10.1109/ACCESS.2018.2822663 10.1016/j.jmsy.2012.06.005 10.1109/TMECH.2017.2728371 10.1109/TIE.2018.2844805 10.1109/TIE.2017.2733438 10.1109/TIA.2017.2655008 10.1016/j.measurement.2017.10.006 10.1016/j.neucom.2015.06.008 10.1109/TFUZZ.2018.2833820 10.1109/TIM.2017.2669947 10.1115/1.4045445 10.1016/j.jmsy.2018.04.005 10.1016/j.jmsy.2017.03.008 10.1109/TII.2017.2672988 10.1016/j.jmsy.2019.02.005 10.1016/j.jmsy.2018.04.007 10.1109/TIE.2006.885131 10.1109/TIE.2019.2891453 10.1016/j.measurement.2014.08.017 10.1109/TIE.2017.2682035 10.1016/j.isatra.2018.12.025 10.1016/j.jmsy.2018.04.008 10.1016/j.jmapro.2017.04.014 10.1109/TPAMI.2016.2537340 10.1016/j.inffus.2017.12.007 10.1109/TIE.2015.2509913 10.1016/j.jmsy.2015.04.008 10.1016/j.ymssp.2004.10.010 10.1109/TIM.2019.2902003 10.1016/j.eswa.2009.06.060 10.1016/j.jmsy.2019.07.005 10.1016/j.jmsy.2013.05.009 10.1016/j.ymssp.2018.03.001 10.1016/j.jmsy.2018.02.004 10.1109/TIE.2017.2745408 10.1109/TIE.2017.2745473 10.1016/j.inffus.2013.10.002 10.1049/iet-epa.2019.0273 10.1109/MSP.2017.2738401 10.1109/TIE.2016.2582729 10.1109/TMECH.2019.2928967 |
| ContentType | Journal Article |
| Copyright | 2020 The Society of Manufacturing Engineers |
| Copyright_xml | – notice: 2020 The Society of Manufacturing Engineers |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.jmsy.2020.04.009 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1878-6642 |
| EndPage | 272 |
| ExternalDocumentID | 10_1016_j_jmsy_2020_04_009 S0278612520300534 |
| GroupedDBID | --K --M -~X .DC .~1 0R~ 1B1 1~. 1~5 29K 3EH 3V. 4.4 457 4G. 5GY 5VS 7-5 71M 7WY 883 88I 8AO 8FE 8FG 8FL 8FW 8G5 8P~ 8R4 8R5 9JN 9M8 AACTN AAEDT AAEDW AAIAV AAIKC AAIKJ AAKOC AALRI AAMNW AAOAW AAQFI AAQXK AAXUO ABFNM ABJCF ABJNI ABMAC ABUWG ABXDB ABYKQ ACDAQ ACGFO ACGFS ACGOD ACIWK ACNNM ACRLP ADBBV ADEZE ADMUD ADTZH AEBSH AECPX AEKER AENEX AFFNX AFKRA AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AIEXJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ ARAPS ASPBG AVWKF AXJTR AZFZN AZQEC BENPR BEZIV BGLVJ BJAXD BKOJK BKOMP BLXMC BPHCQ C1A CCPQU CS3 D-I DU5 DWQXO E3Z EBS EFJIC EFLBG EJD EP2 EP3 FDB FEDTE FGOYB FIRID FNPLU FRNLG FYGXN G-2 GBLVA GNUQQ GROUPED_ABI_INFORM_COMPLETE GROUPED_ABI_INFORM_RESEARCH GUQSH HCIFZ HVGLF HZ~ H~9 IHE J1W JJJVA K60 K6V K6~ K7- KOM L6V LY7 M0C M0F M0N M2O M2P M41 M7S MO0 MS~ N9A O-L O9- OAUVE OZT P-8 P-9 P2P P62 PC. PQBIZ PQQKQ PRG PROAC PTHSS Q2X Q38 R2- RIG ROL RPZ RWL S0X SDF SES SET SPC SPCBC SST SSZ T5K TAE TN5 U5U WH7 WUQ ZHY ~G- 9DU AATTM AAXKI AAYWO AAYXX ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO AEIPS AEUPX AFFHD AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION EFKBS PHGZM PHGZT PQBZA PQGLB ~HD |
| ID | FETCH-LOGICAL-c300t-1d92fead7f4e560ab669aa5185971dae5ad6b35c79c97e8472b6d1aa33d9001a3 |
| ISICitedReferencesCount | 56 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000541121300020&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0278-6125 |
| IngestDate | Sat Nov 29 07:23:38 EST 2025 Tue Nov 18 22:19:43 EST 2025 Fri Feb 23 02:47:54 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | data fusion Induction motor fault diagnosis dynamic routing algorithm multimodal deep learning |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c300t-1d92fead7f4e560ab669aa5185971dae5ad6b35c79c97e8472b6d1aa33d9001a3 |
| PageCount | 9 |
| ParticipantIDs | crossref_primary_10_1016_j_jmsy_2020_04_009 crossref_citationtrail_10_1016_j_jmsy_2020_04_009 elsevier_sciencedirect_doi_10_1016_j_jmsy_2020_04_009 |
| PublicationCentury | 2000 |
| PublicationDate | April 2020 2020-04-00 |
| PublicationDateYYYYMMDD | 2020-04-01 |
| PublicationDate_xml | – month: 04 year: 2020 text: April 2020 |
| PublicationDecade | 2020 |
| PublicationTitle | Journal of manufacturing systems |
| PublicationYear | 2020 |
| Publisher | Elsevier Ltd |
| Publisher_xml | – name: Elsevier Ltd |
| References | Vinayak, Anand, Jagadanand (bib0085) 2020; 14 Tarabini, Scaccabarozzi (bib0185) 2018; 114 Dong, Raed, Li, Xu, Xu (bib0015) 2019; 53 Xia, Li, Xu, Liu, Silva (bib0160) 2018; 23 Jung, Lee, Kwon (bib0030) 2006; 53 Ren, Sun, Cui, Zhang (bib0200) 2018; 48 Wang, Zheng, Wang, Gao (bib0195) 2017; 28 Tuptuk, Hailes (bib0270) 2018; 47 Su, Tao, Jin, Wang, Wang, Wang (bib0110) 2020; 20 Wang, Ma, Zhang, Gao, Wu (bib0005) 2018; 48 Yunusa-Kaltungo, Sinha, Elbhbah (bib0165) 2014; 58 Goodfellow, Bengio, Courville (bib0265) 2016 Zhang, Li, Ding (bib0105) 2019; 95 López-Pérez, Antonino-Daviu (bib0065) 2017; 53 Chen, Song, Guo (bib0145) 2018; 6 Delgado-Arredondo, Morinigo-Sotelo, Osornio-Rios, Avina-Cervantes, Rostro-Gonzalez, Romero-Troncoso (bib0070) 2017; 83 Xu, Liu, Jiang (bib0100) 2020; 69 Chen, Li (bib0240) 2017; 66 Wu, Pigou, Kindermans, Le, Shao, Dambre, Odobez (bib0205) 2016; 38 Irhoumah, Pusca, Lefevre, Mercier, Romary, Demian (bib0225) 2018; 65 Wang, Fu, Zhang, Gao, Zhao (bib0235) 2019; 24 Ji, Wang (bib0020) 2017; 43 Anna, Ottewill, James, Baranowski (bib0090) 2019; 66 Jing, Wang, Zhao, Wang (bib0170) 2017; 17 Ince, Kiranyaz, Eren, Askar, Gabbouj (bib0040) 2016; 63 Zhao, Wang, Yan, Mao, Shen, Wang (bib0140) 2017; 65 Zhao, Yan, Gao (bib0010) 2013; 32 Tian, Morillo, Azarian (bib0095) 2016; 63 Sabour, Frosst, Hinton (bib0155) 2017 Zhang, Li, Gao, Wang, Wen (bib0130) 2018; 48 Ma, Sun, Chen (bib0175) 2018; 14 Salmasi (bib0120) 2017; 64 Jiang, He, Yan, Xie (bib0255) 2019; 66 Sun, Zhao, Yan, Shao, Chen (bib0055) 2017; 13 Singh, Naikan (bib0035) 2018; 110 Shao, Jiang, Zhang, Liang (bib0260) 2018; 65 Zhong, Wong, Yang (bib0210) 2018; 108 Lei, Zuo, He, Zi (bib0215) 2010; 37 Safizadeh, Latifi (bib0125) 2014; 18 Xiao (bib0220) 2017; 17 Li, Sanchez, Zurita, Cerrada, Cabrera, Vasquez (bib0245) 2015; 168 Song, Wang, Chen (bib0075) 2018; 26 Yang, Kim (bib0115) 2006; 20 Jafari-Marandi, Khanzadeh, Tian, Smith, Bian (bib0135) 2019; 51 Ramachandram, Taylor (bib0150) 2017; 34 Charte, Charte, García, Jesus, Herrera (bib0190) 2018; 44 Sharp, Ak, Hedberg (bib0080) 2018; 48 Zadeh (bib0230) 1984; 5 Wang, Liu, Gao, Yan (bib0045) 2012; 31 Wang, Törngren, Onori (bib0025) 2015; 37 Xiong, Zhang, Wan, Liang, Cheng, Liang (bib0180) 2018; 23 Mourtzis, Vlachou, Xanthopoulos, Givehchi, Wang (bib0050) 2016; 39 Wang, Gao, Yan (bib0060) 2014; 46 Ngiam, Khosla, Kim, Nam, Lee, Ng (bib0250) 2011 Ramachandram (10.1016/j.jmsy.2020.04.009_bib0150) 2017; 34 Li (10.1016/j.jmsy.2020.04.009_bib0245) 2015; 168 Xu (10.1016/j.jmsy.2020.04.009_bib0100) 2020; 69 Anna (10.1016/j.jmsy.2020.04.009_bib0090) 2019; 66 Zadeh (10.1016/j.jmsy.2020.04.009_bib0230) 1984; 5 Chen (10.1016/j.jmsy.2020.04.009_bib0145) 2018; 6 Yunusa-Kaltungo (10.1016/j.jmsy.2020.04.009_bib0165) 2014; 58 Zhang (10.1016/j.jmsy.2020.04.009_bib0105) 2019; 95 Wang (10.1016/j.jmsy.2020.04.009_bib0235) 2019; 24 Wang (10.1016/j.jmsy.2020.04.009_bib0045) 2012; 31 Zhao (10.1016/j.jmsy.2020.04.009_bib0010) 2013; 32 Sharp (10.1016/j.jmsy.2020.04.009_bib0080) 2018; 48 Sun (10.1016/j.jmsy.2020.04.009_bib0055) 2017; 13 Wang (10.1016/j.jmsy.2020.04.009_bib0195) 2017; 28 Zhong (10.1016/j.jmsy.2020.04.009_bib0210) 2018; 108 Mourtzis (10.1016/j.jmsy.2020.04.009_bib0050) 2016; 39 Lei (10.1016/j.jmsy.2020.04.009_bib0215) 2010; 37 Xiao (10.1016/j.jmsy.2020.04.009_bib0220) 2017; 17 Wang (10.1016/j.jmsy.2020.04.009_bib0025) 2015; 37 Jing (10.1016/j.jmsy.2020.04.009_bib0170) 2017; 17 Ince (10.1016/j.jmsy.2020.04.009_bib0040) 2016; 63 Irhoumah (10.1016/j.jmsy.2020.04.009_bib0225) 2018; 65 Wang (10.1016/j.jmsy.2020.04.009_bib0060) 2014; 46 Dong (10.1016/j.jmsy.2020.04.009_bib0015) 2019; 53 López-Pérez (10.1016/j.jmsy.2020.04.009_bib0065) 2017; 53 Charte (10.1016/j.jmsy.2020.04.009_bib0190) 2018; 44 Jafari-Marandi (10.1016/j.jmsy.2020.04.009_bib0135) 2019; 51 Xiong (10.1016/j.jmsy.2020.04.009_bib0180) 2018; 23 Yang (10.1016/j.jmsy.2020.04.009_bib0115) 2006; 20 Zhao (10.1016/j.jmsy.2020.04.009_bib0140) 2017; 65 Wang (10.1016/j.jmsy.2020.04.009_bib0005) 2018; 48 Singh (10.1016/j.jmsy.2020.04.009_bib0035) 2018; 110 Chen (10.1016/j.jmsy.2020.04.009_bib0240) 2017; 66 Ngiam (10.1016/j.jmsy.2020.04.009_bib0250) 2011 Su (10.1016/j.jmsy.2020.04.009_bib0110) 2020; 20 Zhang (10.1016/j.jmsy.2020.04.009_bib0130) 2018; 48 Shao (10.1016/j.jmsy.2020.04.009_bib0260) 2018; 65 Goodfellow (10.1016/j.jmsy.2020.04.009_bib0265) 2016 Jiang (10.1016/j.jmsy.2020.04.009_bib0255) 2019; 66 Tuptuk (10.1016/j.jmsy.2020.04.009_bib0270) 2018; 47 Jung (10.1016/j.jmsy.2020.04.009_bib0030) 2006; 53 Sabour (10.1016/j.jmsy.2020.04.009_bib0155) 2017 Delgado-Arredondo (10.1016/j.jmsy.2020.04.009_bib0070) 2017; 83 Safizadeh (10.1016/j.jmsy.2020.04.009_bib0125) 2014; 18 Ji (10.1016/j.jmsy.2020.04.009_bib0020) 2017; 43 Vinayak (10.1016/j.jmsy.2020.04.009_bib0085) 2020; 14 Wu (10.1016/j.jmsy.2020.04.009_bib0205) 2016; 38 Ma (10.1016/j.jmsy.2020.04.009_bib0175) 2018; 14 Tarabini (10.1016/j.jmsy.2020.04.009_bib0185) 2018; 114 Song (10.1016/j.jmsy.2020.04.009_bib0075) 2018; 26 Salmasi (10.1016/j.jmsy.2020.04.009_bib0120) 2017; 64 Tian (10.1016/j.jmsy.2020.04.009_bib0095) 2016; 63 Xia (10.1016/j.jmsy.2020.04.009_bib0160) 2018; 23 Ren (10.1016/j.jmsy.2020.04.009_bib0200) 2018; 48 |
| References_xml | – volume: 38 start-page: 1583 year: 2016 end-page: 1597 ident: bib0205 article-title: Deep dynamic neural networks for multimodal gesture segmentation and recognition publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence – year: 2016 ident: bib0265 article-title: Deep Learning – volume: 44 start-page: 78 year: 2018 end-page: 96 ident: bib0190 article-title: A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines publication-title: Information Fusion – volume: 53 start-page: 291 year: 2019 end-page: 304 ident: bib0015 article-title: A Simple Approach to Multivariate Monitoring of Production Processes with Non-Gaussian Data publication-title: Journal of Manufacturing Systems. – volume: 37 start-page: 517 year: 2015 end-page: 527 ident: bib0025 article-title: Current Status and Advancement of Cyber-Physical Systems in Manufacturing publication-title: Journal of Manufacturing Systems. – volume: 53 start-page: 1842 year: 2006 end-page: 1852 ident: bib0030 article-title: Online diagnosis of induction motors using MCSA publication-title: IEEE Transactions on Industrial Electronics. – volume: 32 start-page: 529 year: 2013 end-page: 535 ident: bib0010 article-title: Dual-scale cascaded adaptive stochastic resonance for rotary machine health monitoring publication-title: Journal of Manufacturing Systems. – volume: 14 start-page: 1137 year: 2018 end-page: 1145 ident: bib0175 article-title: Deep coupling autoencoder for fault diagnosis with multimodal sensory data publication-title: IEEE Transactions on Industrial Informatics. – volume: 65 start-page: 2727 year: 2018 end-page: 2737 ident: bib0260 article-title: Electric locomotive bearing fault diagnosis using a novel convolutional deep belief network publication-title: IEEE Transactions on Industrial Electronics. – start-page: 1 year: 2017 end-page: 11 ident: bib0155 article-title: Dynamic routing between capsules publication-title: i2017 Neural Information Processing Systems (NIPS) Conference – volume: 53 start-page: 1901 year: 2017 end-page: 1908 ident: bib0065 article-title: Application of infrared thermography to failure detection in industrial induction motors: case stories publication-title: IEEE Transactions on Industry Applications. – volume: 13 start-page: 1350 year: 2017 end-page: 1359 ident: bib0055 article-title: Convolutional discriminative feature learning for induction motor fault diagnosis publication-title: IEEE Transactions on Industrial Informatics. – volume: 66 start-page: 1693 year: 2017 end-page: 1702 ident: bib0240 article-title: Multisensor feature fusion for bearing fault diagnosis using sparse autoencoder and deep belief network publication-title: IEEE Transactions on Instrumentation and Measurement. – volume: 110 start-page: 333 year: 2018 end-page: 348 ident: bib0035 article-title: Detection of half broken rotor bar fault in vfd driven induction motor drive using motor square current MUSIC analysis publication-title: Mechanical Systems and Signal Processing. – volume: 48 start-page: 144 year: 2018 end-page: 156 ident: bib0005 article-title: Deep learning for smart manufacturing: methods and applications publication-title: Journal of Manufacturing Systems. – volume: 20 start-page: 403 year: 2006 end-page: 420 ident: bib0115 article-title: Application of Dempster–Shafer theory in fault diagnosis of induction motors using vibration and current signals publication-title: Mechanical Systems and Signal Processing. – volume: 24 start-page: 2139 year: 2019 end-page: 2150 ident: bib0235 article-title: Multilevel Information Fusion for Induction Motor Fault Diagnosis publication-title: IEEE-ASME Transactions on Mechatronics. – volume: 17 year: 2017 ident: bib0220 article-title: A novel evidence theory and fuzzy preference approach-based multi-sensor data fusion technique for fault diagnosis publication-title: Sensors – volume: 65 start-page: 1539 year: 2017 end-page: 1548 ident: bib0140 article-title: Machine health monitoring using local feature-based gated recurrent unit networks publication-title: IEEE Transactions on Industrial Electronics – volume: 17 year: 2017 ident: bib0170 article-title: An adaptive multi-sensor data fusion method based on deep convolutional neural networks for fault diagnosis of planetary gearbox publication-title: Sensors. – volume: 47 start-page: 93 year: 2018 end-page: 106 ident: bib0270 article-title: Security of Smart Manufacturing Systems publication-title: Journal of Manufacturing Systems. – volume: 6 start-page: 20195 year: 2018 end-page: 20208 ident: bib0145 article-title: Attention alignment multimodal LSTM for fine-gained common space learning publication-title: IEEE Access – volume: 108 start-page: 99 year: 2018 end-page: 114 ident: bib0210 article-title: Fault diagnosis of rotating machinery based on multiple probabilistic classifiers publication-title: Mechanical Systems and Signal Processing. – volume: 23 start-page: 101 year: 2018 end-page: 110 ident: bib0160 article-title: Fault diagnosis for rotating machinery using multiple sensors and convolutional neural networks publication-title: IEEE/ASME Transactions on Mechatronics. – volume: 65 start-page: 2642 year: 2018 end-page: 2652 ident: bib0225 article-title: Information fusion with belief functions for detection of interturn short-circuit faults in electrical machines using external flux sensors publication-title: IEEE Transactions on Industrial Electronics – volume: 66 start-page: 9510 year: 2019 end-page: 9520 ident: bib0090 article-title: A PCA and Two-Stage Bayesian Sensor Fusion Approach for Diagnosing Electrical and Mechanical Faults in Induction Motors publication-title: IEEE Transactions on Industrial Electronics – volume: 95 start-page: 295 year: 2019 end-page: 305 ident: bib0105 article-title: Deep residual learning-based fault diagnosis method for rotating machinery publication-title: ISA transaction – volume: 34 start-page: 96 year: 2017 end-page: 108 ident: bib0150 article-title: Deep multimodal learning: a survey on recent advances and trends publication-title: IEEE Signal Processing Magazine – volume: 20 start-page: 1 year: 2020 end-page: 10 ident: bib0110 article-title: Failure Prognosis of Complex Equipment With Multistream Deep Recurrent Neural Network publication-title: Journal of Computing and Information Science in Engineering – volume: 66 start-page: 3196 year: 2019 end-page: 3207 ident: bib0255 article-title: Multiscale convolutional neural networks for fault diagnosis of wind turbine gearbox publication-title: IEEE Transactions on Industrial Electronics. – volume: 63 start-page: 1793 year: 2016 end-page: 1803 ident: bib0095 article-title: Motor Bearing Fault Detection Using Spectral Kurtosis-Based Feature Extraction Coupled With K-Nearest Neighbor Distance Analysis publication-title: IEEE Transactions on Industrial Electronic – start-page: 1 year: 2011 end-page: 8 ident: bib0250 article-title: Multimodal deep learning publication-title: 2011 International Conference on Machine Learning – volume: 14 start-page: 82 year: 2020 end-page: 90 ident: bib0085 article-title: Wavelet-based real-time stator fault detection of inverter-fed induction motor publication-title: IET Electric Power Applications. – volume: 48 start-page: 34 year: 2018 end-page: 50 ident: bib0130 article-title: Imbalanced Data Fault Diagnosis of Rotating Machinery Using Synthetic Oversampling and Feature Learning publication-title: Journal of Manufacturing Systems. – volume: 5 start-page: 81 year: 1984 end-page: 83 ident: bib0230 article-title: Reviews of books: a mathematical theory of evidence publication-title: AI Magazine. – volume: 23 start-page: 506 year: 2018 end-page: 517 ident: bib0180 article-title: Data fusion method based on mutual dimensionless publication-title: IEEE/ASME Transactions on Mechatronics. – volume: 31 start-page: 380 year: 2012 end-page: 387 ident: bib0045 article-title: Current envelope analysis for defect identification and diagnosis in induction motors publication-title: Journal of Manufacturing Systems. – volume: 63 start-page: 7067 year: 2016 end-page: 7075 ident: bib0040 article-title: Real-time motor fault detection by 1-d convolutional neural networks publication-title: IEEE Transactions on Industrial Electronics. – volume: 39 start-page: 1 year: 2016 end-page: 8 ident: bib0050 article-title: Cloud-Based Adaptive Process Planning Considering Availability and Capabilities of Machine Tools publication-title: Journal of Manufacturing Systems. – volume: 37 start-page: 1419 year: 2010 end-page: 1430 ident: bib0215 article-title: A multidimensional hybrid intelligent method for gear fault diagnosis publication-title: Expert Systems with Applications. – volume: 83 start-page: 568 year: 2017 end-page: 589 ident: bib0070 article-title: Methodology for fault detection in induction motors via sound and vibration signals publication-title: Mechanical Systems and Signal Processing. – volume: 43 start-page: 187 year: 2017 end-page: 194 ident: bib0020 article-title: Big Data Analytics Based Fault Prediction for Shop Floor Scheduling publication-title: Journal of Manufacturing Systems. – volume: 58 start-page: 27 year: 2014 end-page: 32 ident: bib0165 article-title: An improved data fusion technique for faults diagnosis in rotating machines publication-title: Measurement. – volume: 46 start-page: 28 year: 2014 end-page: 44 ident: bib0060 article-title: Multi-scale enveloping order spectrogram for rotating machine health diagnosis publication-title: Mechanical Systems and Signal Processing. – volume: 168 start-page: 119 year: 2015 end-page: 127 ident: bib0245 article-title: Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis publication-title: Neurocomputing – volume: 26 start-page: 3467 year: 2018 end-page: 3478 ident: bib0075 article-title: Step-by-step fuzzy diagnosis method for equipment based on symptom extraction and trivalent logic fuzzy diagnosis theory publication-title: IEEE Transactions on Fuzzy Systems. – volume: 18 start-page: 1 year: 2014 end-page: 8 ident: bib0125 article-title: Using multi-sensor data fusion for vibration fault diagnosis of rolling element bearings by accelerometer and load cell publication-title: Information fusion – volume: 28 start-page: 472 year: 2017 end-page: 478 ident: bib0195 article-title: A virtual sensing based augmented particle filter for tool condition prognosis publication-title: Journal of Manufacturing Processes. – volume: 69 start-page: 509 year: 2020 end-page: 520 ident: bib0100 article-title: Online Fault Diagnosis Method Based on Transfer Convolutional Neural Networks publication-title: IEEE Transactions on Instrumentation and Measurement – volume: 64 start-page: 6105 year: 2017 end-page: 6115 ident: bib0120 article-title: A self-healing induction motor drive with model free sensor tampering and sensor fault detection, isolation, and compensation publication-title: IEEE Transactions on Industrial Electronics. – volume: 114 start-page: 409 year: 2018 end-page: 416 ident: bib0185 article-title: Uncertainty-based combination of signal processing techniques for the identification of rotor imbalance publication-title: Measurement. – volume: 48 start-page: 170 year: 2018 end-page: 179 ident: bib0080 article-title: A Survey of The Advancing Use and Development of Machine Learning in Smart Manufacturing publication-title: Journal of Manufacturing Systems. – volume: 48 start-page: 71 year: 2018 end-page: 77 ident: bib0200 article-title: Bearing Remaining Useful Life Prediction Based on Deep Autoencoder and Deep Neural Networks publication-title: Journal of Manufacturing Systems. – volume: 51 start-page: 29 year: 2019 end-page: 41 ident: bib0135 article-title: From in-situ Monitoring toward High-Throughput Process Control: Cost-Driven Decision-Making Framework for Laser-Based Additive Manufacturing publication-title: Journal of Manufacturing Systems. – volume: 14 start-page: 1137 issue: 3 year: 2018 ident: 10.1016/j.jmsy.2020.04.009_bib0175 article-title: Deep coupling autoencoder for fault diagnosis with multimodal sensory data publication-title: IEEE Transactions on Industrial Informatics. doi: 10.1109/TII.2018.2793246 – volume: 108 start-page: 99 year: 2018 ident: 10.1016/j.jmsy.2020.04.009_bib0210 article-title: Fault diagnosis of rotating machinery based on multiple probabilistic classifiers publication-title: Mechanical Systems and Signal Processing. doi: 10.1016/j.ymssp.2018.02.009 – volume: 23 start-page: 506 issue: 2 year: 2018 ident: 10.1016/j.jmsy.2020.04.009_bib0180 article-title: Data fusion method based on mutual dimensionless publication-title: IEEE/ASME Transactions on Mechatronics. doi: 10.1109/TMECH.2017.2759791 – volume: 46 start-page: 28 issue: 1 year: 2014 ident: 10.1016/j.jmsy.2020.04.009_bib0060 article-title: Multi-scale enveloping order spectrogram for rotating machine health diagnosis publication-title: Mechanical Systems and Signal Processing. doi: 10.1016/j.ymssp.2013.06.001 – volume: 48 start-page: 144 year: 2018 ident: 10.1016/j.jmsy.2020.04.009_bib0005 article-title: Deep learning for smart manufacturing: methods and applications publication-title: Journal of Manufacturing Systems. doi: 10.1016/j.jmsy.2018.01.003 – volume: 39 start-page: 1 year: 2016 ident: 10.1016/j.jmsy.2020.04.009_bib0050 article-title: Cloud-Based Adaptive Process Planning Considering Availability and Capabilities of Machine Tools publication-title: Journal of Manufacturing Systems. doi: 10.1016/j.jmsy.2016.01.003 – volume: 83 start-page: 568 issue: 15 year: 2017 ident: 10.1016/j.jmsy.2020.04.009_bib0070 article-title: Methodology for fault detection in induction motors via sound and vibration signals publication-title: Mechanical Systems and Signal Processing. doi: 10.1016/j.ymssp.2016.06.032 – volume: 6 start-page: 20195 year: 2018 ident: 10.1016/j.jmsy.2020.04.009_bib0145 article-title: Attention alignment multimodal LSTM for fine-gained common space learning publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2822663 – start-page: 1 year: 2017 ident: 10.1016/j.jmsy.2020.04.009_bib0155 article-title: Dynamic routing between capsules publication-title: i2017 Neural Information Processing Systems (NIPS) Conference – volume: 31 start-page: 380 issue: 4 year: 2012 ident: 10.1016/j.jmsy.2020.04.009_bib0045 article-title: Current envelope analysis for defect identification and diagnosis in induction motors publication-title: Journal of Manufacturing Systems. doi: 10.1016/j.jmsy.2012.06.005 – volume: 23 start-page: 101 issue: 1 year: 2018 ident: 10.1016/j.jmsy.2020.04.009_bib0160 article-title: Fault diagnosis for rotating machinery using multiple sensors and convolutional neural networks publication-title: IEEE/ASME Transactions on Mechatronics. doi: 10.1109/TMECH.2017.2728371 – volume: 66 start-page: 3196 issue: 4 year: 2019 ident: 10.1016/j.jmsy.2020.04.009_bib0255 article-title: Multiscale convolutional neural networks for fault diagnosis of wind turbine gearbox publication-title: IEEE Transactions on Industrial Electronics. doi: 10.1109/TIE.2018.2844805 – volume: 65 start-page: 1539 issue: 2 year: 2017 ident: 10.1016/j.jmsy.2020.04.009_bib0140 article-title: Machine health monitoring using local feature-based gated recurrent unit networks publication-title: IEEE Transactions on Industrial Electronics doi: 10.1109/TIE.2017.2733438 – volume: 53 start-page: 1901 issue: 3 year: 2017 ident: 10.1016/j.jmsy.2020.04.009_bib0065 article-title: Application of infrared thermography to failure detection in industrial induction motors: case stories publication-title: IEEE Transactions on Industry Applications. doi: 10.1109/TIA.2017.2655008 – volume: 114 start-page: 409 year: 2018 ident: 10.1016/j.jmsy.2020.04.009_bib0185 article-title: Uncertainty-based combination of signal processing techniques for the identification of rotor imbalance publication-title: Measurement. doi: 10.1016/j.measurement.2017.10.006 – volume: 168 start-page: 119 year: 2015 ident: 10.1016/j.jmsy.2020.04.009_bib0245 article-title: Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis publication-title: Neurocomputing doi: 10.1016/j.neucom.2015.06.008 – volume: 26 start-page: 3467 issue: 6 year: 2018 ident: 10.1016/j.jmsy.2020.04.009_bib0075 article-title: Step-by-step fuzzy diagnosis method for equipment based on symptom extraction and trivalent logic fuzzy diagnosis theory publication-title: IEEE Transactions on Fuzzy Systems. doi: 10.1109/TFUZZ.2018.2833820 – volume: 66 start-page: 1693 issue: 7 year: 2017 ident: 10.1016/j.jmsy.2020.04.009_bib0240 article-title: Multisensor feature fusion for bearing fault diagnosis using sparse autoencoder and deep belief network publication-title: IEEE Transactions on Instrumentation and Measurement. doi: 10.1109/TIM.2017.2669947 – volume: 20 start-page: 1 issue: 2 year: 2020 ident: 10.1016/j.jmsy.2020.04.009_bib0110 article-title: Failure Prognosis of Complex Equipment With Multistream Deep Recurrent Neural Network publication-title: Journal of Computing and Information Science in Engineering doi: 10.1115/1.4045445 – volume: 48 start-page: 34 year: 2018 ident: 10.1016/j.jmsy.2020.04.009_bib0130 article-title: Imbalanced Data Fault Diagnosis of Rotating Machinery Using Synthetic Oversampling and Feature Learning publication-title: Journal of Manufacturing Systems. doi: 10.1016/j.jmsy.2018.04.005 – volume: 43 start-page: 187 year: 2017 ident: 10.1016/j.jmsy.2020.04.009_bib0020 article-title: Big Data Analytics Based Fault Prediction for Shop Floor Scheduling publication-title: Journal of Manufacturing Systems. doi: 10.1016/j.jmsy.2017.03.008 – volume: 13 start-page: 1350 issue: 3 year: 2017 ident: 10.1016/j.jmsy.2020.04.009_bib0055 article-title: Convolutional discriminative feature learning for induction motor fault diagnosis publication-title: IEEE Transactions on Industrial Informatics. doi: 10.1109/TII.2017.2672988 – volume: 51 start-page: 29 year: 2019 ident: 10.1016/j.jmsy.2020.04.009_bib0135 article-title: From in-situ Monitoring toward High-Throughput Process Control: Cost-Driven Decision-Making Framework for Laser-Based Additive Manufacturing publication-title: Journal of Manufacturing Systems. doi: 10.1016/j.jmsy.2019.02.005 – volume: 47 start-page: 93 year: 2018 ident: 10.1016/j.jmsy.2020.04.009_bib0270 article-title: Security of Smart Manufacturing Systems publication-title: Journal of Manufacturing Systems. doi: 10.1016/j.jmsy.2018.04.007 – volume: 53 start-page: 1842 issue: 6 year: 2006 ident: 10.1016/j.jmsy.2020.04.009_bib0030 article-title: Online diagnosis of induction motors using MCSA publication-title: IEEE Transactions on Industrial Electronics. doi: 10.1109/TIE.2006.885131 – volume: 66 start-page: 9510 issue: 12 year: 2019 ident: 10.1016/j.jmsy.2020.04.009_bib0090 article-title: A PCA and Two-Stage Bayesian Sensor Fusion Approach for Diagnosing Electrical and Mechanical Faults in Induction Motors publication-title: IEEE Transactions on Industrial Electronics doi: 10.1109/TIE.2019.2891453 – volume: 58 start-page: 27 year: 2014 ident: 10.1016/j.jmsy.2020.04.009_bib0165 article-title: An improved data fusion technique for faults diagnosis in rotating machines publication-title: Measurement. doi: 10.1016/j.measurement.2014.08.017 – volume: 64 start-page: 6105 issue: 8 year: 2017 ident: 10.1016/j.jmsy.2020.04.009_bib0120 article-title: A self-healing induction motor drive with model free sensor tampering and sensor fault detection, isolation, and compensation publication-title: IEEE Transactions on Industrial Electronics. doi: 10.1109/TIE.2017.2682035 – start-page: 1 year: 2011 ident: 10.1016/j.jmsy.2020.04.009_bib0250 article-title: Multimodal deep learning publication-title: 2011 International Conference on Machine Learning – volume: 95 start-page: 295 year: 2019 ident: 10.1016/j.jmsy.2020.04.009_bib0105 article-title: Deep residual learning-based fault diagnosis method for rotating machinery publication-title: ISA transaction doi: 10.1016/j.isatra.2018.12.025 – volume: 48 start-page: 71 year: 2018 ident: 10.1016/j.jmsy.2020.04.009_bib0200 article-title: Bearing Remaining Useful Life Prediction Based on Deep Autoencoder and Deep Neural Networks publication-title: Journal of Manufacturing Systems. doi: 10.1016/j.jmsy.2018.04.008 – volume: 28 start-page: 472 issue: 3 year: 2017 ident: 10.1016/j.jmsy.2020.04.009_bib0195 article-title: A virtual sensing based augmented particle filter for tool condition prognosis publication-title: Journal of Manufacturing Processes. doi: 10.1016/j.jmapro.2017.04.014 – volume: 38 start-page: 1583 issue: 8 year: 2016 ident: 10.1016/j.jmsy.2020.04.009_bib0205 article-title: Deep dynamic neural networks for multimodal gesture segmentation and recognition publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence doi: 10.1109/TPAMI.2016.2537340 – volume: 44 start-page: 78 year: 2018 ident: 10.1016/j.jmsy.2020.04.009_bib0190 article-title: A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines publication-title: Information Fusion doi: 10.1016/j.inffus.2017.12.007 – volume: 63 start-page: 1793 issue: 3 year: 2016 ident: 10.1016/j.jmsy.2020.04.009_bib0095 article-title: Motor Bearing Fault Detection Using Spectral Kurtosis-Based Feature Extraction Coupled With K-Nearest Neighbor Distance Analysis publication-title: IEEE Transactions on Industrial Electronic doi: 10.1109/TIE.2015.2509913 – volume: 37 start-page: 517 year: 2015 ident: 10.1016/j.jmsy.2020.04.009_bib0025 article-title: Current Status and Advancement of Cyber-Physical Systems in Manufacturing publication-title: Journal of Manufacturing Systems. doi: 10.1016/j.jmsy.2015.04.008 – volume: 20 start-page: 403 issue: 2 year: 2006 ident: 10.1016/j.jmsy.2020.04.009_bib0115 article-title: Application of Dempster–Shafer theory in fault diagnosis of induction motors using vibration and current signals publication-title: Mechanical Systems and Signal Processing. doi: 10.1016/j.ymssp.2004.10.010 – volume: 69 start-page: 509 issue: 2 year: 2020 ident: 10.1016/j.jmsy.2020.04.009_bib0100 article-title: Online Fault Diagnosis Method Based on Transfer Convolutional Neural Networks publication-title: IEEE Transactions on Instrumentation and Measurement doi: 10.1109/TIM.2019.2902003 – volume: 5 start-page: 81 issue: 3 year: 1984 ident: 10.1016/j.jmsy.2020.04.009_bib0230 article-title: Reviews of books: a mathematical theory of evidence publication-title: AI Magazine. – volume: 17 issue: 2504 year: 2017 ident: 10.1016/j.jmsy.2020.04.009_bib0220 article-title: A novel evidence theory and fuzzy preference approach-based multi-sensor data fusion technique for fault diagnosis publication-title: Sensors – volume: 37 start-page: 1419 issue: 2 year: 2010 ident: 10.1016/j.jmsy.2020.04.009_bib0215 article-title: A multidimensional hybrid intelligent method for gear fault diagnosis publication-title: Expert Systems with Applications. doi: 10.1016/j.eswa.2009.06.060 – year: 2016 ident: 10.1016/j.jmsy.2020.04.009_bib0265 – volume: 53 start-page: 291 year: 2019 ident: 10.1016/j.jmsy.2020.04.009_bib0015 article-title: A Simple Approach to Multivariate Monitoring of Production Processes with Non-Gaussian Data publication-title: Journal of Manufacturing Systems. doi: 10.1016/j.jmsy.2019.07.005 – volume: 32 start-page: 529 issue: 4 year: 2013 ident: 10.1016/j.jmsy.2020.04.009_bib0010 article-title: Dual-scale cascaded adaptive stochastic resonance for rotary machine health monitoring publication-title: Journal of Manufacturing Systems. doi: 10.1016/j.jmsy.2013.05.009 – volume: 110 start-page: 333 issue: 15 year: 2018 ident: 10.1016/j.jmsy.2020.04.009_bib0035 article-title: Detection of half broken rotor bar fault in vfd driven induction motor drive using motor square current MUSIC analysis publication-title: Mechanical Systems and Signal Processing. doi: 10.1016/j.ymssp.2018.03.001 – volume: 48 start-page: 170 year: 2018 ident: 10.1016/j.jmsy.2020.04.009_bib0080 article-title: A Survey of The Advancing Use and Development of Machine Learning in Smart Manufacturing publication-title: Journal of Manufacturing Systems. doi: 10.1016/j.jmsy.2018.02.004 – volume: 65 start-page: 2642 issue: 3 year: 2018 ident: 10.1016/j.jmsy.2020.04.009_bib0225 article-title: Information fusion with belief functions for detection of interturn short-circuit faults in electrical machines using external flux sensors publication-title: IEEE Transactions on Industrial Electronics doi: 10.1109/TIE.2017.2745408 – volume: 65 start-page: 2727 issue: 3 year: 2018 ident: 10.1016/j.jmsy.2020.04.009_bib0260 article-title: Electric locomotive bearing fault diagnosis using a novel convolutional deep belief network publication-title: IEEE Transactions on Industrial Electronics. doi: 10.1109/TIE.2017.2745473 – volume: 18 start-page: 1 year: 2014 ident: 10.1016/j.jmsy.2020.04.009_bib0125 article-title: Using multi-sensor data fusion for vibration fault diagnosis of rolling element bearings by accelerometer and load cell publication-title: Information fusion doi: 10.1016/j.inffus.2013.10.002 – volume: 17 issue: 144 year: 2017 ident: 10.1016/j.jmsy.2020.04.009_bib0170 article-title: An adaptive multi-sensor data fusion method based on deep convolutional neural networks for fault diagnosis of planetary gearbox publication-title: Sensors. – volume: 14 start-page: 82 issue: 1 year: 2020 ident: 10.1016/j.jmsy.2020.04.009_bib0085 article-title: Wavelet-based real-time stator fault detection of inverter-fed induction motor publication-title: IET Electric Power Applications. doi: 10.1049/iet-epa.2019.0273 – volume: 34 start-page: 96 issue: 6 year: 2017 ident: 10.1016/j.jmsy.2020.04.009_bib0150 article-title: Deep multimodal learning: a survey on recent advances and trends publication-title: IEEE Signal Processing Magazine doi: 10.1109/MSP.2017.2738401 – volume: 63 start-page: 7067 issue: 11 year: 2016 ident: 10.1016/j.jmsy.2020.04.009_bib0040 article-title: Real-time motor fault detection by 1-d convolutional neural networks publication-title: IEEE Transactions on Industrial Electronics. doi: 10.1109/TIE.2016.2582729 – volume: 24 start-page: 2139 issue: 5 year: 2019 ident: 10.1016/j.jmsy.2020.04.009_bib0235 article-title: Multilevel Information Fusion for Induction Motor Fault Diagnosis publication-title: IEEE-ASME Transactions on Mechatronics. doi: 10.1109/TMECH.2019.2928967 |
| SSID | ssj0012402 |
| Score | 2.461273 |
| Snippet | •Dynamic routing-based multimodal neural network for multi-sensory data fusion.•>Multimodal feature extraction schema is designed to enhance the diagnostic... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 264 |
| SubjectTerms | data fusion dynamic routing algorithm Induction motor fault diagnosis multimodal deep learning |
| Title | Dynamic Routing-based Multimodal Neural Network for Multi-sensory Fault Diagnosis of Induction Motor |
| URI | https://dx.doi.org/10.1016/j.jmsy.2020.04.009 |
| Volume | 55 |
| WOSCitedRecordID | wos000541121300020&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: 1878-6642 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0012402 issn: 0278-6125 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9NAEF6FlgMcEE-1vLQHbpEre70P77GCVlBBhUQRuVnrtY0SJU7VJFX7S_p3mdldOw6NKkDiYsebrL2a-TIznp0HIe-MKOrEahEltcwiLrmOMqNMZKwxMqt5UQvrmk2o09NsNNJfB4ObNhfmcqqaJru60uf_ldUwBszG1Nm_YHd3UxiAz8B0OALb4fhHjP_ge8wPMdYH9FKEeqocukTb2bwEjmA9DndyAeAuztB9Gy3glRa33I8NXIIwdEF4vl4JdvjwTcW_zH194m0m7cw0K8yU8KmPi14xdETIyscDj6erDpA_grf6ZNxMAKc_b7mxR-Mtg5_NuAgFw4O_gsW9MBfnRGsTadZRSyjrGLzMorHl1ZKXxRmOSb4hrIXoS1tfAD0obuZ7AN3SCd49MTmYzBbXB7giV9s21msN2MUlfsN14DJYjGX8U36P7DIlNIjL3cNPR6OTboMKN6Wc-y6sO-Rj-dDB35-03ebp2TFnj8mjwC166IHzhAyq5il52CtL-YyUAUJ0A0J0DSHqIUQDhChAiG5AiDoI0Q5CdF7TDkLUQeg5-X58dPb-YxS6cUQWaLGMklKzGuSOqnkFZrIppNTGCLD3tEpKUwlTyiIVVmmrVQVGDytkmRiTpqUGgpn0Bdlp5k21R6hklpeMV1UVZ7yQMd5KccOUia2wPNsnSUuv3IZS9dgxZZq3MYmTHGmcI43zmOdA430y7Oac-0Itd_5atGzIg6npTcgcUHPHvJf_OO8VebD-L7wmO8uLVfWG3LeXy_Hi4m0A1y_Kuac3 |
| 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=Dynamic+Routing-based+Multimodal+Neural+Network+for+Multi-sensory+Fault+Diagnosis+of+Induction+Motor&rft.jtitle=Journal+of+manufacturing+systems&rft.au=Fu%2C+Peilun&rft.au=Wang%2C+Jinjiang&rft.au=Zhang%2C+Xing&rft.au=Zhang%2C+Laibin&rft.date=2020-04-01&rft.pub=Elsevier+Ltd&rft.issn=0278-6125&rft.eissn=1878-6642&rft.volume=55&rft.spage=264&rft.epage=272&rft_id=info:doi/10.1016%2Fj.jmsy.2020.04.009&rft.externalDocID=S0278612520300534 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0278-6125&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0278-6125&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0278-6125&client=summon |