Deep imbalanced domain adaptation for transfer learning fault diagnosis of bearings under multiple working conditions
The tremendous success of deep learning and transfer learning broadened the scope of fault diagnosis, especially the latter further improved the diagnosis accuracy under multiple working conditions. However, most existing attempts assume that label distribution is domain-invariant despite taking int...
Saved in:
| Published in: | Reliability engineering & system safety Vol. 230; p. 108890 |
|---|---|
| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Barking
Elsevier Ltd
01.02.2023
Elsevier BV |
| Subjects: | |
| ISSN: | 0951-8320, 1879-0836 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | The tremendous success of deep learning and transfer learning broadened the scope of fault diagnosis, especially the latter further improved the diagnosis accuracy under multiple working conditions. However, most existing attempts assume that label distribution is domain-invariant despite taking into account the different feature distributions. This does not accommodate the diversity of fault mode distributions under different operating conditions and weakens the generalization to imbalanced domain adaptation (IDA) scenarios. Therefore, this work proposed a novel deep imbalanced domain adaptation (DIDA) framework for fault diagnosis of bearings, aiming at the challenging scenario where feature shift and label shift exist simultaneously under different working conditions. Specifically, DIDA overcomes the class-imbalanced label shift and achieves a fine-grained latent space matching by cost-sensitive learning and categorical alignment. Besides, margin loss regularization is introduced to further optimize classification boundaries and improve cross-domain generalization for IDA fault diagnosis tasks. Finally, we simulated the IDA protocols on experimental datasets and conducted case studies under multiple working conditions, thus validating the effectiveness and superiority of the proposed framework.
•A novel deep imbalanced domain adaptation (DIDA) is proposed.•DIDA narrows both feature shift and label shift.•DIDA broadens fault diagnosis to IDA scenarios.•Experimental case studies verified validity and superiority. |
|---|---|
| AbstractList | The tremendous success of deep learning and transfer learning broadened the scope of fault diagnosis, especially the latter further improved the diagnosis accuracy under multiple working conditions. However, most existing attempts assume that label distribution is domain-invariant despite taking into account the different feature distributions. This does not accommodate the diversity of fault mode distributions under different operating conditions and weakens the generalization to imbalanced domain adaptation (IDA) scenarios. Therefore, this work proposed a novel deep imbalanced domain adaptation (DIDA) framework for fault diagnosis of bearings, aiming at the challenging scenario where feature shift and label shift exist simultaneously under different working conditions. Specifically, DIDA overcomes the class-imbalanced label shift and achieves a fine-grained latent space matching by cost-sensitive learning and categorical alignment. Besides, margin loss regularization is introduced to further optimize classification boundaries and improve cross-domain generalization for IDA fault diagnosis tasks. Finally, we simulated the IDA protocols on experimental datasets and conducted case studies under multiple working conditions, thus validating the effectiveness and superiority of the proposed framework.
•A novel deep imbalanced domain adaptation (DIDA) is proposed.•DIDA narrows both feature shift and label shift.•DIDA broadens fault diagnosis to IDA scenarios.•Experimental case studies verified validity and superiority. The tremendous success of deep learning and transfer learning broadened the scope of fault diagnosis, especially the latter further improved the diagnosis accuracy under multiple working conditions. However, most existing attempts assume that label distribution is domain-invariant despite taking into account the different feature distributions. This does not accommodate the diversity of fault mode distributions under different operating conditions and weakens the generalization to imbalanced domain adaptation (IDA) scenarios. Therefore, this work proposed a novel deep imbalanced domain adaptation (DIDA) framework for fault diagnosis of bearings, aiming at the challenging scenario where feature shift and label shift exist simultaneously under different working conditions. Specifically, DIDA overcomes the class-imbalanced label shift and achieves a fine-grained latent space matching by cost-sensitive learning and categorical alignment. Besides, margin loss regularization is introduced to further optimize classification boundaries and improve cross-domain generalization for IDA fault diagnosis tasks. Finally, we simulated the IDA protocols on experimental datasets and conducted case studies under multiple working conditions, thus validating the effectiveness and superiority of the proposed framework. |
| ArticleNumber | 108890 |
| Author | Cao, Yudong Zhuang, Jichao Zhao, Xiaoli Ding, Yifei Jia, Minping Lee, Chi-Guhn |
| Author_xml | – sequence: 1 givenname: Yifei orcidid: 0000-0001-8408-6945 surname: Ding fullname: Ding, Yifei organization: School of Mechanical Engineering, Southeast University, Nanjing 211189, PR China – sequence: 2 givenname: Minping orcidid: 0000-0001-9010-2307 surname: Jia fullname: Jia, Minping email: mpjia@seu.edu.cn organization: School of Mechanical Engineering, Southeast University, Nanjing 211189, PR China – sequence: 3 givenname: Jichao surname: Zhuang fullname: Zhuang, Jichao organization: School of Mechanical Engineering, Southeast University, Nanjing 211189, PR China – sequence: 4 givenname: Yudong orcidid: 0000-0003-2167-8075 surname: Cao fullname: Cao, Yudong organization: School of Mechanical Engineering, Southeast University, Nanjing 211189, PR China – sequence: 5 givenname: Xiaoli surname: Zhao fullname: Zhao, Xiaoli organization: School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210014, PR China – sequence: 6 givenname: Chi-Guhn orcidid: 0000-0002-0916-0241 surname: Lee fullname: Lee, Chi-Guhn organization: Centre for Maintenance Optimization and Reliability Engineering, University of Toronto, Toronto M5S 3G8, Canada |
| BookMark | eNp9kM1KxDAURoMoOKO-gKuA645pM20ScCPjLwhudB3S5EZSO0lNWsW3N6WuXLgK3PudG76zRoc-eEDovCSbkpTNZbeJkNKmIlWVB5wLcoBWJWeiIJw2h2hFRF0WnFbkGK1T6gghW1GzFZpuAAbs9q3qlddgsAl75TxWRg2jGl3w2IaIx6h8shBxDyp659-wVVM_YuPUmw_JJRwsbvMurxKevMnRfQ64oQf8FeL7jOjgjZtPplN0ZFWf4Oz3PUGvd7cvu4fi6fn-cXf9VGha8bEwQjWUEwaN2NZbJRpNW8tsC8JQUtVaA9F5bQWjbW1NbYBpxUvFcznRlC09QRfL3SGGjwnSKLswRZ-_lBVrsqeGUZZTfEnpGFKKYKV2S_dc2_WyJHKWLDs5S5azZLlIzmj1Bx2i26v4_T90tUCQq386iDJpB7N9F0GP0gT3H_4DTvya3A |
| CitedBy_id | crossref_primary_10_1016_j_ress_2023_109490 crossref_primary_10_1016_j_eswa_2024_124944 crossref_primary_10_3390_en16062668 crossref_primary_10_1016_j_engappai_2024_107973 crossref_primary_10_1088_1361_6501_ad7a1b crossref_primary_10_1016_j_aei_2024_102878 crossref_primary_10_1016_j_ress_2025_111050 crossref_primary_10_1109_TNNLS_2024_3362687 crossref_primary_10_1109_JIOT_2024_3387741 crossref_primary_10_1177_14759217251345714 crossref_primary_10_1016_j_ress_2023_109522 crossref_primary_10_1016_j_knosys_2025_114109 crossref_primary_10_1016_j_ress_2023_109486 crossref_primary_10_1016_j_ress_2024_110400 crossref_primary_10_1088_1361_6501_adb321 crossref_primary_10_1016_j_ress_2023_109246 crossref_primary_10_1016_j_ress_2024_110369 crossref_primary_10_1016_j_eswa_2024_125594 crossref_primary_10_1088_1361_6501_ad20c3 crossref_primary_10_1177_14759217251332517 crossref_primary_10_1007_s12206_025_0810_4 crossref_primary_10_1016_j_apacoust_2025_110877 crossref_primary_10_1016_j_ress_2023_109380 crossref_primary_10_1088_1361_6501_ad2668 crossref_primary_10_1016_j_aei_2024_103048 crossref_primary_10_1088_1361_6501_ad36d9 crossref_primary_10_1109_TIM_2025_3551574 crossref_primary_10_1016_j_engappai_2025_110510 crossref_primary_10_1109_JSEN_2023_3256060 crossref_primary_10_1109_TIM_2024_3400354 crossref_primary_10_1007_s13042_024_02233_0 crossref_primary_10_1038_s41598_024_79151_2 crossref_primary_10_1016_j_neucom_2025_129588 crossref_primary_10_1016_j_ress_2024_110653 crossref_primary_10_3390_act14010039 crossref_primary_10_1016_j_ress_2023_109256 crossref_primary_10_1016_j_engappai_2025_111964 crossref_primary_10_1016_j_ress_2024_109991 crossref_primary_10_1016_j_ymssp_2025_113202 crossref_primary_10_1016_j_ress_2025_110999 crossref_primary_10_1088_1361_6501_acce55 crossref_primary_10_1016_j_ress_2024_110539 crossref_primary_10_1016_j_measurement_2025_116869 crossref_primary_10_1088_1361_6501_ad3fd4 crossref_primary_10_3390_app132413120 crossref_primary_10_1016_j_measurement_2024_116344 crossref_primary_10_1016_j_knosys_2023_110918 crossref_primary_10_1016_j_ress_2024_110181 crossref_primary_10_1016_j_ress_2024_110749 crossref_primary_10_1109_JSEN_2025_3529034 crossref_primary_10_1016_j_ress_2023_109746 crossref_primary_10_1038_s41598_023_50826_6 crossref_primary_10_1016_j_ymssp_2024_111837 crossref_primary_10_1016_j_ress_2023_109468 crossref_primary_10_1016_j_ress_2024_110347 crossref_primary_10_1016_j_ress_2024_110745 crossref_primary_10_1108_ILT_04_2023_0086 crossref_primary_10_1007_s11071_024_09733_2 crossref_primary_10_1016_j_engappai_2024_109380 crossref_primary_10_3390_s24134070 crossref_primary_10_1109_TIM_2023_3271736 crossref_primary_10_3390_e26121007 crossref_primary_10_1177_09544062241245826 crossref_primary_10_1016_j_engappai_2024_108678 crossref_primary_10_1016_j_ress_2024_109938 crossref_primary_10_1109_JSEN_2023_3343077 crossref_primary_10_1088_1361_6501_ae02ba crossref_primary_10_3390_act13100401 crossref_primary_10_1016_j_ymssp_2025_112934 crossref_primary_10_3390_machines12110787 crossref_primary_10_3390_s25123686 crossref_primary_10_1016_j_psep_2025_107704 crossref_primary_10_1038_s41598_025_03459_w crossref_primary_10_1093_jcde_qwae105 crossref_primary_10_3389_fbioe_2024_1334643 crossref_primary_10_1016_j_engappai_2024_109371 crossref_primary_10_1016_j_engappai_2023_107539 crossref_primary_10_1016_j_jmapro_2025_05_084 crossref_primary_10_1016_j_eswa_2024_123930 crossref_primary_10_1016_j_ress_2024_110562 crossref_primary_10_1088_1361_6501_ad19c2 crossref_primary_10_1088_1361_6501_adc7d0 crossref_primary_10_1016_j_aei_2024_102436 crossref_primary_10_1016_j_engappai_2023_107538 crossref_primary_10_1080_10589759_2025_2487927 crossref_primary_10_1016_j_ress_2023_109601 crossref_primary_10_1016_j_ymssp_2025_112422 crossref_primary_10_1016_j_ress_2025_110842 crossref_primary_10_1007_s41060_023_00463_z crossref_primary_10_1016_j_ress_2024_110293 crossref_primary_10_1109_TASE_2025_3571516 crossref_primary_10_1109_ACCESS_2024_3413578 crossref_primary_10_1109_TIM_2024_3462979 crossref_primary_10_1109_JSEN_2023_3347251 crossref_primary_10_1016_j_measurement_2025_118117 crossref_primary_10_1088_1361_6501_add75b crossref_primary_10_1016_j_measurement_2024_115901 crossref_primary_10_1016_j_ress_2023_109618 crossref_primary_10_1016_j_engappai_2024_109396 crossref_primary_10_1088_1361_6501_ace9f0 crossref_primary_10_1088_3049_4761_adce0d crossref_primary_10_1109_ACCESS_2023_3264636 crossref_primary_10_1016_j_aei_2024_102681 crossref_primary_10_1016_j_measurement_2025_116982 crossref_primary_10_1016_j_asoc_2024_112528 crossref_primary_10_1016_j_knosys_2025_114251 crossref_primary_10_1016_j_cie_2025_110988 crossref_primary_10_1016_j_knosys_2024_111682 crossref_primary_10_1016_j_ress_2025_111648 crossref_primary_10_1093_jcde_qwad076 crossref_primary_10_1016_j_knosys_2025_113165 crossref_primary_10_1016_j_ress_2023_109692 crossref_primary_10_1016_j_energy_2025_137825 crossref_primary_10_1016_j_apacoust_2023_109797 crossref_primary_10_1088_1361_6501_ad7a92 crossref_primary_10_1016_j_eswa_2025_126423 crossref_primary_10_3390_app132312823 crossref_primary_10_1016_j_aei_2024_103079 crossref_primary_10_1088_1361_6501_ace841 crossref_primary_10_32604_cmes_2023_031360 crossref_primary_10_1016_j_measurement_2023_112945 crossref_primary_10_1016_j_ress_2024_110380 crossref_primary_10_1109_TII_2023_3343735 crossref_primary_10_1007_s11432_024_4333_7 crossref_primary_10_1016_j_ress_2023_109705 crossref_primary_10_1016_j_ress_2024_110707 crossref_primary_10_1016_j_ress_2024_110708 crossref_primary_10_1109_JSEN_2024_3479213 crossref_primary_10_3390_s23135875 crossref_primary_10_1088_1361_6501_adcce6 crossref_primary_10_1088_1361_6501_ad99f3 crossref_primary_10_1016_j_engappai_2023_106670 crossref_primary_10_1016_j_eswa_2024_124240 crossref_primary_10_1177_14759217241279789 crossref_primary_10_1016_j_compind_2025_104349 crossref_primary_10_1109_TII_2023_3331129 crossref_primary_10_1016_j_ress_2025_111145 crossref_primary_10_1109_TIM_2025_3551907 crossref_primary_10_1109_TII_2025_3545106 crossref_primary_10_1016_j_aei_2025_103400 crossref_primary_10_1108_SRT_11_2024_0017 crossref_primary_10_1371_journal_pone_0307672 crossref_primary_10_3390_app14209208 crossref_primary_10_1088_1361_6501_ad4d15 crossref_primary_10_1109_JPHOT_2024_3392392 crossref_primary_10_1016_j_ress_2023_109832 crossref_primary_10_1109_JIOT_2024_3419044 crossref_primary_10_1177_14759217241256690 crossref_primary_10_1016_j_ress_2025_110854 crossref_primary_10_1109_JSEN_2023_3280202 crossref_primary_10_1038_s41598_025_92838_4 crossref_primary_10_1016_j_ress_2025_110979 crossref_primary_10_1177_14759217251330518 |
| Cites_doi | 10.1109/ACCESS.2020.2990528 10.1016/j.ymssp.2021.108018 10.1007/978-3-319-58347-1_10 10.1016/j.ymssp.2020.107233 10.1007/s10845-020-01600-2 10.1016/j.ress.2021.107583 10.1007/978-3-030-01219-9_18 10.1016/S0165-1765(01)00524-9 10.1016/j.ress.2018.02.012 10.1109/TKDE.2009.191 10.1016/j.eswa.2021.116459 10.1109/CVPR42600.2020.00763 10.1016/j.isatra.2021.02.042 10.1109/TIM.2022.3216413 10.1109/CVPR.2019.00949 10.1016/j.ress.2021.107530 10.1016/j.ymssp.2021.108616 10.1016/j.measurement.2021.109834 10.1613/jair.953 10.1016/j.ress.2021.108012 10.1016/j.measurement.2021.110511 10.1016/j.ress.2021.108126 10.1109/TSMC.2017.2754287 10.1109/CVPR.2016.580 10.1016/j.measurement.2021.109352 10.1016/j.knosys.2022.109272 10.1109/TII.2019.2943898 10.1088/1361-6501/ac57ef 10.1016/j.ress.2021.107938 10.1016/j.ymssp.2017.08.002 10.1007/s00521-019-04097-w 10.1016/j.isatra.2019.08.012 10.1016/j.ymssp.2020.106683 10.1016/j.ress.2021.107934 |
| ContentType | Journal Article |
| Copyright | 2022 Elsevier Ltd Copyright Elsevier BV Feb 2023 |
| Copyright_xml | – notice: 2022 Elsevier Ltd – notice: Copyright Elsevier BV Feb 2023 |
| DBID | AAYXX CITATION 7ST 7TB 8FD C1K FR3 SOI |
| DOI | 10.1016/j.ress.2022.108890 |
| DatabaseName | CrossRef Environment Abstracts Mechanical & Transportation Engineering Abstracts Technology Research Database Environmental Sciences and Pollution Management Engineering Research Database Environment Abstracts |
| DatabaseTitle | CrossRef Engineering Research Database Technology Research Database Mechanical & Transportation Engineering Abstracts Environment Abstracts Environmental Sciences and Pollution Management |
| DatabaseTitleList | Engineering Research Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1879-0836 |
| ExternalDocumentID | 10_1016_j_ress_2022_108890 S0951832022005075 |
| GroupedDBID | --K --M .~1 0R~ 123 1B1 1~. 1~5 29P 4.4 457 4G. 5VS 7-5 71M 8P~ 9JN 9JO AABNK AACTN AAEDT AAEDW AAFJI AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AAXUO ABEFU ABFNM ABJNI ABMAC ABMMH ABTAH ABXDB ABYKQ ACDAQ ACGFS ACIWK ACNNM ACRLP ADBBV ADEZE ADMUD ADTZH AEBSH AECPX AEKER AENEX AFKWA AFRAH AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AIEXJ AIKHN AITUG AJBFU AJOXV AKYCK ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOMHK ASPBG AVARZ AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q GBLVA HVGLF HZ~ IHE J1W JJJVA KOM LY7 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. PRBVW Q38 R2- RIG ROL RPZ SDF SDG SES SET SEW SPC SPCBC SSB SSO SST SSZ T5K TN5 WUQ XPP ZMT ZY4 ~G- 9DU AATTM AAXKI AAYWO AAYXX ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION EFKBS ~HD 7ST 7TB 8FD AGCQF C1K FR3 SOI |
| ID | FETCH-LOGICAL-c328t-d9a63807e69454a96c3bf7fbe9d3025cce0c807f973b5fd5de7ca81a8049961b3 |
| ISICitedReferencesCount | 166 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000951956500002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0951-8320 |
| IngestDate | Wed Aug 13 06:35:28 EDT 2025 Tue Nov 18 19:44:10 EST 2025 Sat Nov 29 07:10:28 EST 2025 Fri Feb 23 02:39:26 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Fault diagnosis Domain shift Bearings Label shift Imbalanced domain adaptation |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c328t-d9a63807e69454a96c3bf7fbe9d3025cce0c807f973b5fd5de7ca81a8049961b3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0001-8408-6945 0000-0001-9010-2307 0000-0002-0916-0241 0000-0003-2167-8075 |
| PQID | 2760886737 |
| PQPubID | 2045406 |
| ParticipantIDs | proquest_journals_2760886737 crossref_citationtrail_10_1016_j_ress_2022_108890 crossref_primary_10_1016_j_ress_2022_108890 elsevier_sciencedirect_doi_10_1016_j_ress_2022_108890 |
| PublicationCentury | 2000 |
| PublicationDate | February 2023 2023-02-00 20230201 |
| PublicationDateYYYYMMDD | 2023-02-01 |
| PublicationDate_xml | – month: 02 year: 2023 text: February 2023 |
| PublicationDecade | 2020 |
| PublicationPlace | Barking |
| PublicationPlace_xml | – name: Barking |
| PublicationTitle | Reliability engineering & system safety |
| PublicationYear | 2023 |
| Publisher | Elsevier Ltd Elsevier BV |
| Publisher_xml | – name: Elsevier Ltd – name: Elsevier BV |
| References | Pan, Yang (b17) 2010; 22 Zhu, Zhuang, Wang, Ke, Chen, Bian (b43) 2020 Zhao, Zhong, Fu, Tang, Pecht (b9) 2020; 16 Chen, Mauricio, Li, Gryllias (b10) 2020; 140 Ding, Jia, Miao, Huang (b6) 2021; 212 Kang B, Xie S, Rohrbach M, Yan Z, Gordo A, Feng J, et al. Decoupling Representation and Classifier for Long-Tailed Recognition. In: International conference on learning representations. 2019. Wang, Lan, Liu, Ouyang, Qin (b16) 2021 Ding, Jia, Miao, Cao (b13) 2022; 168 Chen, Zhang, Gao (b11) 2021; 32 Han, Liu, Yang, Jiang (b22) 2020; 97 Cao K, Wei C, Gaidon A, Arechiga N, Ma T. Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss. In: Proceedings of the 33rd international conference on neural information processing systems. 2019, p. 1567–78. Wu, Zhao, Sun, Yan, Chen (b27) 2021; 216 Tan, Peng, Saenko (b34) 2020 Xu, Saleh (b1) 2021; 211 Wang, Zhou, Du, Lei, Wang (b5) 2022; 162 Kuang, Xu, Tao, Wu (b31) 2022; 71 Zou Y, Yu Z, Kumar BVKV, Wang J. Unsupervised Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training. In: Proceedings of the European conference on computer vision. 2018, p. 289–305. Manjurul Islam, Kim (b4) 2019; 184 Fu, Zhang, Lin, Zhao, Zhong (b7) 2021; 216 Long, Cao, Wang, Jordan (b32) 2015 Chawla, Bowyer, Hall, Kegelmeyer (b38) 2002; 16 Zellinger, Grubinger, Lughofer, Natschläger, Saminger-Platz (b33) 2019 Qian, Qin, Wang, Liu (b19) 2021; 178 Yang, Yang, Wang, Cao, Zou, Xie (b23) 2021 Cui Y, Jia M, Lin T-Y, Song Y, Belongie S. Class-Balanced Loss Based on Effective Number of Samples. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019, p. 9268–77. Bao, Du, Zhang, Wang, Qiu, Cao (b14) 2021 Wu, Zhang, Guo, Ji, Pecht (b28) 2022; 193 Wen, Gao, Li (b18) 2019; 49 Jamal MA, Brown M, Yang M-H, Wang L, Gong B. Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition From a Domain Adaptation Perspective. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020, p. 7610–9. Zhao, Wang, Cai, Zhang, Wang, Du (b2) 2022; 188 Huang C, Li Y, Loy CC, Tang X. Learning Deep Representation for Imbalanced Classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2016, p. 5375–84. Shao, Jiang, Zhang, Duan, Liang, Wu (b8) 2018; 100 Reed (b44) 2001; 74 Ding, Zhuang, Ding, Jia (b3) 2022; 218 Mao, Feng, Liu, Zhang, Liang (b12) 2021; 150 Wen, Li, Gao (b25) 2020; 32 Xia, Shao, Williams, Lu, Shu, de Silva (b21) 2021; 215 Liu, Chen, Zhang, Liu, He, Zhou (b30) 2022; 251 Neupane, Seok (b15) 2020; 8 Ganin, Ustinova, Ajakan, Germain, Larochelle, Laviolette (b45) 2017; 17 Wang, Cui, Cai, Li (b20) 2022; 71 Wang Y-X, Ramanan D, Hebert M. Learning to Model the Tail. In: Proceedings of the 31st international conference on neural information processing systems. NIPS’17, Long Beach, California, USA; ISBN: 978-1-5108-6096-4, 2017, p. 7032–42. Zhu, Dong, Pan, Hu, Zhu (b24) 2022; 33 Tan, Guo, Gao, Lin, Liu (b29) 2021; 183 Zhang, Chen, Li, Zhang, Lv, He (b26) 2022; 119 Neupane (10.1016/j.ress.2022.108890_b15) 2020; 8 Wen (10.1016/j.ress.2022.108890_b18) 2019; 49 Shao (10.1016/j.ress.2022.108890_b8) 2018; 100 Kuang (10.1016/j.ress.2022.108890_b31) 2022; 71 Chen (10.1016/j.ress.2022.108890_b10) 2020; 140 Ganin (10.1016/j.ress.2022.108890_b45) 2017; 17 10.1016/j.ress.2022.108890_b39 Chawla (10.1016/j.ress.2022.108890_b38) 2002; 16 Yang (10.1016/j.ress.2022.108890_b23) 2021 Wang (10.1016/j.ress.2022.108890_b16) 2021 10.1016/j.ress.2022.108890_b36 10.1016/j.ress.2022.108890_b35 10.1016/j.ress.2022.108890_b37 Zellinger (10.1016/j.ress.2022.108890_b33) 2019 Zhu (10.1016/j.ress.2022.108890_b24) 2022; 33 Tan (10.1016/j.ress.2022.108890_b34) 2020 Liu (10.1016/j.ress.2022.108890_b30) 2022; 251 Wu (10.1016/j.ress.2022.108890_b28) 2022; 193 Mao (10.1016/j.ress.2022.108890_b12) 2021; 150 Zhu (10.1016/j.ress.2022.108890_b43) 2020 Ding (10.1016/j.ress.2022.108890_b6) 2021; 212 Wang (10.1016/j.ress.2022.108890_b20) 2022; 71 Reed (10.1016/j.ress.2022.108890_b44) 2001; 74 Chen (10.1016/j.ress.2022.108890_b11) 2021; 32 Zhang (10.1016/j.ress.2022.108890_b26) 2022; 119 Tan (10.1016/j.ress.2022.108890_b29) 2021; 183 10.1016/j.ress.2022.108890_b42 Xia (10.1016/j.ress.2022.108890_b21) 2021; 215 Wen (10.1016/j.ress.2022.108890_b25) 2020; 32 Wu (10.1016/j.ress.2022.108890_b27) 2021; 216 Zhao (10.1016/j.ress.2022.108890_b2) 2022; 188 Xu (10.1016/j.ress.2022.108890_b1) 2021; 211 Wang (10.1016/j.ress.2022.108890_b5) 2022; 162 Fu (10.1016/j.ress.2022.108890_b7) 2021; 216 10.1016/j.ress.2022.108890_b41 Bao (10.1016/j.ress.2022.108890_b14) 2021 10.1016/j.ress.2022.108890_b40 Han (10.1016/j.ress.2022.108890_b22) 2020; 97 Long (10.1016/j.ress.2022.108890_b32) 2015 Pan (10.1016/j.ress.2022.108890_b17) 2010; 22 Qian (10.1016/j.ress.2022.108890_b19) 2021; 178 Zhao (10.1016/j.ress.2022.108890_b9) 2020; 16 Ding (10.1016/j.ress.2022.108890_b13) 2022; 168 Ding (10.1016/j.ress.2022.108890_b3) 2022; 218 Manjurul Islam (10.1016/j.ress.2022.108890_b4) 2019; 184 |
| References_xml | – volume: 162 year: 2022 ident: b5 article-title: Bearing fault diagnosis method based on adaptive maximum cyclostationarity blind deconvolution publication-title: Mech Syst Signal Process – start-page: 585 year: 2020 end-page: 602 ident: b34 article-title: Class-imbalanced domain adaptation: an empirical odyssey publication-title: Computer vision – ECCV 2020 workshops – volume: 71 start-page: 1 year: 2022 end-page: 10 ident: b20 article-title: Partial transfer learning of multidiscriminator deep weighted adversarial network in cross-machine fault diagnosis publication-title: IEEE Trans Instrum Meas – volume: 215 year: 2021 ident: b21 article-title: Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning publication-title: Rel Eng Syst Saf – reference: Cao K, Wei C, Gaidon A, Arechiga N, Ma T. Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss. In: Proceedings of the 33rd international conference on neural information processing systems. 2019, p. 1567–78. – year: 2021 ident: b16 article-title: Generalizing to unseen domains: A survey on domain generalization – reference: Zou Y, Yu Z, Kumar BVKV, Wang J. Unsupervised Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training. In: Proceedings of the European conference on computer vision. 2018, p. 289–305. – start-page: 97 year: 2015 end-page: 105 ident: b32 article-title: Learning transferable features with deep adaptation networks publication-title: 32nd international conference on machine learning, vol. 1 – volume: 178 year: 2021 ident: b19 article-title: A new deep transfer learning network based on convolutional auto-encoder for mechanical fault diagnosis publication-title: Measurement – volume: 119 start-page: 152 year: 2022 end-page: 171 ident: b26 article-title: Intelligent fault diagnosis of machines with small & imbalanced data: A state-of-the-art review and possible extensions publication-title: ISA Trans – reference: Wang Y-X, Ramanan D, Hebert M. Learning to Model the Tail. In: Proceedings of the 31st international conference on neural information processing systems. NIPS’17, Long Beach, California, USA; ISBN: 978-1-5108-6096-4, 2017, p. 7032–42. – volume: 218 year: 2022 ident: b3 article-title: Self-supervised pretraining via contrast learning for intelligent incipient fault detection of bearings publication-title: Rel Eng Syst Saf – volume: 74 start-page: 15 year: 2001 end-page: 19 ident: b44 article-title: The Pareto, Zipf and other power laws publication-title: Econom Lett – volume: 97 start-page: 269 year: 2020 end-page: 281 ident: b22 article-title: Deep transfer network with joint distribution adaptation: A new intelligent fault diagnosis framework for industry application publication-title: ISA Trans – volume: 251 year: 2022 ident: b30 article-title: Cross-domain intelligent bearing fault diagnosis under class imbalanced samples via transfer residual network augmented with explicit weight self-assignment strategy based on meta data publication-title: Knowl-Based Syst – volume: 216 year: 2021 ident: b7 article-title: Deep residual LSTM with domain-invariance for remaining useful life prediction across domains publication-title: Rel Eng Syst Saf – volume: 183 year: 2021 ident: b29 article-title: MiDAN: A framework for cross-domain intelligent fault diagnosis with imbalanced datasets publication-title: Measurement – reference: Cui Y, Jia M, Lin T-Y, Song Y, Belongie S. Class-Balanced Loss Based on Effective Number of Samples. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019, p. 9268–77. – volume: 211 year: 2021 ident: b1 article-title: Machine learning for reliability engineering and safety applications: Review of current status and future opportunities publication-title: Rel Eng Syst Saf – volume: 150 year: 2021 ident: b12 article-title: A new deep auto-encoder method with fusing discriminant information for bearing fault diagnosis publication-title: Mech Syst Signal Process – start-page: 1 year: 2021 end-page: 12 ident: b23 article-title: Advancing imbalanced domain adaptation: Cluster-level discrepancy minimization with a comprehensive benchmark publication-title: IEEE Trans Cybern – volume: 193 year: 2022 ident: b28 article-title: Imbalanced bearing fault diagnosis under variant working conditions using cost-sensitive deep domain adaptation network publication-title: Expert Syst Appl – start-page: 1 year: 2020 end-page: 10 ident: b43 article-title: Deep subdomain adaptation network for image classification publication-title: IEEE Trans Neural Netw Learn Syst – volume: 168 year: 2022 ident: b13 article-title: A novel time– frequency transformer based on self– attention mechanism and its application in fault diagnosis of rolling bearings publication-title: Mech Syst Signal Process – volume: 32 start-page: 971 year: 2021 end-page: 987 ident: b11 article-title: Bearing fault diagnosis base on multi-scale CNN and LSTM model publication-title: J Intell Manuf – volume: 8 start-page: 93155 year: 2020 end-page: 93178 ident: b15 article-title: Bearing fault detection and diagnosis using case western reserve university dataset with deep learning approaches: A review publication-title: IEEE Access – volume: 17 start-page: 189 year: 2017 end-page: 209 ident: b45 article-title: Domain-adversarial training of neural networks publication-title: Adv Comput Vis Pattern Recognit – volume: 32 start-page: 6111 year: 2020 end-page: 6124 ident: b25 article-title: A transfer convolutional neural network for fault diagnosis based on ResNet-50 publication-title: Neural Comput Appl – volume: 71 start-page: 1 year: 2022 end-page: 11 ident: b31 article-title: Class-imbalance adversarial transfer learning network for cross-domain fault diagnosis with imbalanced data publication-title: IEEE Trans Instrum Meas – reference: Kang B, Xie S, Rohrbach M, Yan Z, Gordo A, Feng J, et al. Decoupling Representation and Classifier for Long-Tailed Recognition. In: International conference on learning representations. 2019. – volume: 16 start-page: 321 year: 2002 end-page: 357 ident: b38 article-title: SMOTE: Synthetic minority over-sampling technique publication-title: J Artificial Intelligence Res – reference: Huang C, Li Y, Loy CC, Tang X. Learning Deep Representation for Imbalanced Classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2016, p. 5375–84. – volume: 140 year: 2020 ident: b10 article-title: A deep learning method for bearing fault diagnosis based on cyclic spectral coherence and convolutional neural networks publication-title: Mech Syst Signal Process – volume: 49 start-page: 136 year: 2019 end-page: 144 ident: b18 article-title: A new deep transfer learning based on sparse auto-encoder for fault diagnosis publication-title: IEEE Trans Syst Man Cybern Syst – volume: 188 year: 2022 ident: b2 article-title: Multiscale inverted residual convolutional neural network for intelligent diagnosis of bearings under variable load condition publication-title: Measurement – volume: 216 year: 2021 ident: b27 article-title: Learning from class-imbalanced data with a model-agnostic framework for machine intelligent diagnosis publication-title: Rel Eng Syst Saf – volume: 16 start-page: 4681 year: 2020 end-page: 4690 ident: b9 article-title: Deep residual shrinkage networks for fault diagnosis publication-title: IEEE Trans Ind Inf – start-page: 65 year: 2021 end-page: 79 ident: b14 article-title: A transformer model-based approach to bearing fault diagnosis publication-title: Data science – volume: 184 start-page: 55 year: 2019 end-page: 66 ident: b4 article-title: Reliable multiple combined fault diagnosis of bearings using heterogeneous feature models and multiclass support vector machines publication-title: Rel Eng Syst Saf – volume: 100 start-page: 743 year: 2018 end-page: 765 ident: b8 article-title: Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing publication-title: Mech Syst Signal Process – volume: 22 start-page: 1345 year: 2010 end-page: 1359 ident: b17 article-title: A survey on transfer learning publication-title: IEEE Trans Knowl Data Eng – volume: 33 year: 2022 ident: b24 article-title: A simulation-data-driven subdomain adaptation adversarial transfer learning network for rolling element bearing fault diagnosis publication-title: Meas Sci Technol – reference: Jamal MA, Brown M, Yang M-H, Wang L, Gong B. Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition From a Domain Adaptation Perspective. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020, p. 7610–9. – volume: 212 year: 2021 ident: b6 article-title: Remaining useful life estimation using deep metric transfer learning for kernel regression publication-title: Rel Eng Syst Saf – year: 2019 ident: b33 article-title: Central moment discrepancy (CMD) for domain-invariant representation learning – volume: 8 start-page: 93155 year: 2020 ident: 10.1016/j.ress.2022.108890_b15 article-title: Bearing fault detection and diagnosis using case western reserve university dataset with deep learning approaches: A review publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2990528 – volume: 162 year: 2022 ident: 10.1016/j.ress.2022.108890_b5 article-title: Bearing fault diagnosis method based on adaptive maximum cyclostationarity blind deconvolution publication-title: Mech Syst Signal Process doi: 10.1016/j.ymssp.2021.108018 – volume: 17 start-page: 189 year: 2017 ident: 10.1016/j.ress.2022.108890_b45 article-title: Domain-adversarial training of neural networks publication-title: Adv Comput Vis Pattern Recognit doi: 10.1007/978-3-319-58347-1_10 – volume: 150 year: 2021 ident: 10.1016/j.ress.2022.108890_b12 article-title: A new deep auto-encoder method with fusing discriminant information for bearing fault diagnosis publication-title: Mech Syst Signal Process doi: 10.1016/j.ymssp.2020.107233 – volume: 71 start-page: 1 year: 2022 ident: 10.1016/j.ress.2022.108890_b31 article-title: Class-imbalance adversarial transfer learning network for cross-domain fault diagnosis with imbalanced data publication-title: IEEE Trans Instrum Meas – volume: 32 start-page: 971 issue: 4 year: 2021 ident: 10.1016/j.ress.2022.108890_b11 article-title: Bearing fault diagnosis base on multi-scale CNN and LSTM model publication-title: J Intell Manuf doi: 10.1007/s10845-020-01600-2 – volume: 212 year: 2021 ident: 10.1016/j.ress.2022.108890_b6 article-title: Remaining useful life estimation using deep metric transfer learning for kernel regression publication-title: Rel Eng Syst Saf doi: 10.1016/j.ress.2021.107583 – ident: 10.1016/j.ress.2022.108890_b37 doi: 10.1007/978-3-030-01219-9_18 – start-page: 97 year: 2015 ident: 10.1016/j.ress.2022.108890_b32 article-title: Learning transferable features with deep adaptation networks – volume: 74 start-page: 15 issue: 1 year: 2001 ident: 10.1016/j.ress.2022.108890_b44 article-title: The Pareto, Zipf and other power laws publication-title: Econom Lett doi: 10.1016/S0165-1765(01)00524-9 – ident: 10.1016/j.ress.2022.108890_b39 – volume: 184 start-page: 55 year: 2019 ident: 10.1016/j.ress.2022.108890_b4 article-title: Reliable multiple combined fault diagnosis of bearings using heterogeneous feature models and multiclass support vector machines publication-title: Rel Eng Syst Saf doi: 10.1016/j.ress.2018.02.012 – volume: 22 start-page: 1345 issue: 10 year: 2010 ident: 10.1016/j.ress.2022.108890_b17 article-title: A survey on transfer learning publication-title: IEEE Trans Knowl Data Eng doi: 10.1109/TKDE.2009.191 – start-page: 1 year: 2021 ident: 10.1016/j.ress.2022.108890_b23 article-title: Advancing imbalanced domain adaptation: Cluster-level discrepancy minimization with a comprehensive benchmark publication-title: IEEE Trans Cybern – volume: 193 year: 2022 ident: 10.1016/j.ress.2022.108890_b28 article-title: Imbalanced bearing fault diagnosis under variant working conditions using cost-sensitive deep domain adaptation network publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2021.116459 – ident: 10.1016/j.ress.2022.108890_b40 doi: 10.1109/CVPR42600.2020.00763 – volume: 119 start-page: 152 year: 2022 ident: 10.1016/j.ress.2022.108890_b26 article-title: Intelligent fault diagnosis of machines with small & imbalanced data: A state-of-the-art review and possible extensions publication-title: ISA Trans doi: 10.1016/j.isatra.2021.02.042 – volume: 71 start-page: 1 year: 2022 ident: 10.1016/j.ress.2022.108890_b20 article-title: Partial transfer learning of multidiscriminator deep weighted adversarial network in cross-machine fault diagnosis publication-title: IEEE Trans Instrum Meas doi: 10.1109/TIM.2022.3216413 – ident: 10.1016/j.ress.2022.108890_b35 – ident: 10.1016/j.ress.2022.108890_b42 doi: 10.1109/CVPR.2019.00949 – start-page: 585 year: 2020 ident: 10.1016/j.ress.2022.108890_b34 article-title: Class-imbalanced domain adaptation: an empirical odyssey – volume: 211 year: 2021 ident: 10.1016/j.ress.2022.108890_b1 article-title: Machine learning for reliability engineering and safety applications: Review of current status and future opportunities publication-title: Rel Eng Syst Saf doi: 10.1016/j.ress.2021.107530 – volume: 168 year: 2022 ident: 10.1016/j.ress.2022.108890_b13 article-title: A novel time– frequency transformer based on self– attention mechanism and its application in fault diagnosis of rolling bearings publication-title: Mech Syst Signal Process doi: 10.1016/j.ymssp.2021.108616 – volume: 183 year: 2021 ident: 10.1016/j.ress.2022.108890_b29 article-title: MiDAN: A framework for cross-domain intelligent fault diagnosis with imbalanced datasets publication-title: Measurement doi: 10.1016/j.measurement.2021.109834 – volume: 16 start-page: 321 year: 2002 ident: 10.1016/j.ress.2022.108890_b38 article-title: SMOTE: Synthetic minority over-sampling technique publication-title: J Artificial Intelligence Res doi: 10.1613/jair.953 – volume: 216 year: 2021 ident: 10.1016/j.ress.2022.108890_b7 article-title: Deep residual LSTM with domain-invariance for remaining useful life prediction across domains publication-title: Rel Eng Syst Saf doi: 10.1016/j.ress.2021.108012 – year: 2021 ident: 10.1016/j.ress.2022.108890_b16 – volume: 188 year: 2022 ident: 10.1016/j.ress.2022.108890_b2 article-title: Multiscale inverted residual convolutional neural network for intelligent diagnosis of bearings under variable load condition publication-title: Measurement doi: 10.1016/j.measurement.2021.110511 – volume: 218 year: 2022 ident: 10.1016/j.ress.2022.108890_b3 article-title: Self-supervised pretraining via contrast learning for intelligent incipient fault detection of bearings publication-title: Rel Eng Syst Saf doi: 10.1016/j.ress.2021.108126 – volume: 49 start-page: 136 issue: 1 year: 2019 ident: 10.1016/j.ress.2022.108890_b18 article-title: A new deep transfer learning based on sparse auto-encoder for fault diagnosis publication-title: IEEE Trans Syst Man Cybern Syst doi: 10.1109/TSMC.2017.2754287 – ident: 10.1016/j.ress.2022.108890_b36 doi: 10.1109/CVPR.2016.580 – start-page: 65 year: 2021 ident: 10.1016/j.ress.2022.108890_b14 article-title: A transformer model-based approach to bearing fault diagnosis – volume: 178 year: 2021 ident: 10.1016/j.ress.2022.108890_b19 article-title: A new deep transfer learning network based on convolutional auto-encoder for mechanical fault diagnosis publication-title: Measurement doi: 10.1016/j.measurement.2021.109352 – volume: 251 year: 2022 ident: 10.1016/j.ress.2022.108890_b30 article-title: Cross-domain intelligent bearing fault diagnosis under class imbalanced samples via transfer residual network augmented with explicit weight self-assignment strategy based on meta data publication-title: Knowl-Based Syst doi: 10.1016/j.knosys.2022.109272 – start-page: 1 year: 2020 ident: 10.1016/j.ress.2022.108890_b43 article-title: Deep subdomain adaptation network for image classification publication-title: IEEE Trans Neural Netw Learn Syst – volume: 16 start-page: 4681 issue: 7 year: 2020 ident: 10.1016/j.ress.2022.108890_b9 article-title: Deep residual shrinkage networks for fault diagnosis publication-title: IEEE Trans Ind Inf doi: 10.1109/TII.2019.2943898 – volume: 33 issue: 7 year: 2022 ident: 10.1016/j.ress.2022.108890_b24 article-title: A simulation-data-driven subdomain adaptation adversarial transfer learning network for rolling element bearing fault diagnosis publication-title: Meas Sci Technol doi: 10.1088/1361-6501/ac57ef – ident: 10.1016/j.ress.2022.108890_b41 – volume: 215 year: 2021 ident: 10.1016/j.ress.2022.108890_b21 article-title: Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning publication-title: Rel Eng Syst Saf doi: 10.1016/j.ress.2021.107938 – volume: 100 start-page: 743 year: 2018 ident: 10.1016/j.ress.2022.108890_b8 article-title: Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing publication-title: Mech Syst Signal Process doi: 10.1016/j.ymssp.2017.08.002 – year: 2019 ident: 10.1016/j.ress.2022.108890_b33 – volume: 32 start-page: 6111 issue: 10 year: 2020 ident: 10.1016/j.ress.2022.108890_b25 article-title: A transfer convolutional neural network for fault diagnosis based on ResNet-50 publication-title: Neural Comput Appl doi: 10.1007/s00521-019-04097-w – volume: 97 start-page: 269 year: 2020 ident: 10.1016/j.ress.2022.108890_b22 article-title: Deep transfer network with joint distribution adaptation: A new intelligent fault diagnosis framework for industry application publication-title: ISA Trans doi: 10.1016/j.isatra.2019.08.012 – volume: 140 year: 2020 ident: 10.1016/j.ress.2022.108890_b10 article-title: A deep learning method for bearing fault diagnosis based on cyclic spectral coherence and convolutional neural networks publication-title: Mech Syst Signal Process doi: 10.1016/j.ymssp.2020.106683 – volume: 216 year: 2021 ident: 10.1016/j.ress.2022.108890_b27 article-title: Learning from class-imbalanced data with a model-agnostic framework for machine intelligent diagnosis publication-title: Rel Eng Syst Saf doi: 10.1016/j.ress.2021.107934 |
| SSID | ssj0004957 |
| Score | 2.6853285 |
| Snippet | The tremendous success of deep learning and transfer learning broadened the scope of fault diagnosis, especially the latter further improved the diagnosis... |
| SourceID | proquest crossref elsevier |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 108890 |
| SubjectTerms | Adaptation Bearings Deep learning Domain shift Domains Fault diagnosis Imbalanced domain adaptation Label shift Machine learning Regularization Reliability engineering Transfer learning Working conditions |
| Title | Deep imbalanced domain adaptation for transfer learning fault diagnosis of bearings under multiple working conditions |
| URI | https://dx.doi.org/10.1016/j.ress.2022.108890 https://www.proquest.com/docview/2760886737 |
| Volume | 230 |
| WOSCitedRecordID | wos000951956500002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1879-0836 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0004957 issn: 0951-8320 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9NAEF6FlAMcEE9RWtAe6MlyFXtt7-6xoomgCoFDWqUny95H5Sq1TZq05W_xC9lnHCqI4MDFirx2Yu18Wc9-880MAO8TETO1BKAQZZKHCeM0pKXUBDwRRPBBJEyPpbMxnkzIbEa_9no_fC7MzRzXNbm7o-1_NbU6p4ytU2f_wdzrL1Un1GdldHVUZlfHvzL8sRBtUF2VWrOog_u8uVK7_6DgRbupLDQOq1j4thEXgSxW86XmYrX0zlYpKdWYaeupM80Wnfjw1jLsWrLOq47x83W-xbyy1b-_B6Ird2hAZgtHB9eFdPVHjBPt-qqcV1JUa0mPVfF-rurWv10Nv71yBPeJ1vs3XQjFUL7nK964ix2TESMvfvb02jrF5uwXmjIK1apjYzfCLtIEU1NVe3MVj114x67D0W_fDpaouDzURMaheoJYKywJHXTvQh__n3zJR6fjcT4dzqYHaNR-C3WfMh3PP0DHFjMPwE6MU0r6YOfo03B20mXjUltf1j-5S9WyqsL7P_0nd-ieY2C8nelT8MRtU-CRhdcz0BP1c_B4o3jlC7DSQIMd0KAFGuyABhXQoAca9ECDBmhwDTTYSOiBBg3QoAcadECDHdBegtPRcPrhY-iaeIQMxWQZclpkuqmByGiSJgXNGCollqWgHCl_mzExYGpYUozKVPKUC8wKEhVE78WzqESvQL9uavEawAEiMiUSJ5QonyphZRkXBSYJKrnELOG7IPJzmTNX4V43WpnnXsp4mev5z_X853b-d0Gwvqe19V22Xp16E-XOQ7WeZ64gtvW-fW_P3C0VahxnalD3iXqzfXgPPOr-Lvugv1ysxFvwkN0sq-vFOwe_n96Xv-c |
| 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=Deep+imbalanced+domain+adaptation+for+transfer+learning+fault+diagnosis+of+bearings+under+multiple+working+conditions&rft.jtitle=Reliability+engineering+%26+system+safety&rft.au=Ding%2C+Yifei&rft.au=Jia%2C+Minping&rft.au=Zhuang%2C+Jichao&rft.au=Cao%2C+Yudong&rft.date=2023-02-01&rft.pub=Elsevier+BV&rft.issn=0951-8320&rft.eissn=1879-0836&rft.volume=230&rft.spage=1&rft_id=info:doi/10.1016%2Fj.ress.2022.108890&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0951-8320&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0951-8320&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0951-8320&client=summon |