Unsupervised domain adaptive bearing fault diagnosis based on maximum domain discrepancy
Abstract In the existing domain adaptation-based bearing fault diagnosis methods, the data difference between the source domain and the target domain is not obvious. Besides, parameters of target domain feature extractor gradually approach that of source domain feature extractor to cheat discriminat...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2024-01-01
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| Series: | EURASIP Journal on Advances in Signal Processing |
| Online Access: | https://doi.org/10.1186/s13634-023-01107-x |
| _version_ | 1797355444393476096 |
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| author | Cuixiang Wang Shengkai Wu Xing Shao |
| author_facet | Cuixiang Wang Shengkai Wu Xing Shao |
| author_sort | Cuixiang Wang |
| collection | DOAJ |
| description | Abstract In the existing domain adaptation-based bearing fault diagnosis methods, the data difference between the source domain and the target domain is not obvious. Besides, parameters of target domain feature extractor gradually approach that of source domain feature extractor to cheat discriminator which results in similar feature distribution of source domain and target domain. These issues make it difficult for the domain adaptation-based bearing fault diagnosis methods to achieve satisfactory performance. An unsupervised domain adaptive bearing fault diagnosis method based on maximum domain discrepancy (UDA-BFD-MDD) is proposed in this paper. In UDA-BFD-MDD, maximum domain discrepancy is exploited to maximize the feature difference between the source domain and target domain, while the output feature of target domain feature extractor can cheat the discriminator. The performance of UDA-BFD-MDD is verified through comprehensive experiments using the bearing dataset of Case Western Reserve University. The experimental results demonstrate that UDA-BFD-MDD is more stable during training process and can achieve higher accuracy rate. |
| first_indexed | 2024-03-08T14:11:20Z |
| format | Article |
| id | doaj.art-87143e7b59fc4612939b1a298325b37d |
| institution | Directory Open Access Journal |
| issn | 1687-6180 |
| language | English |
| last_indexed | 2024-03-08T14:11:20Z |
| publishDate | 2024-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Advances in Signal Processing |
| spelling | doaj.art-87143e7b59fc4612939b1a298325b37d2024-01-14T12:42:27ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61802024-01-012024111910.1186/s13634-023-01107-xUnsupervised domain adaptive bearing fault diagnosis based on maximum domain discrepancyCuixiang Wang0Shengkai Wu1Xing Shao2School of Information Engineering, Yancheng Institute of TechnologySchool of Information Engineering, Yancheng Institute of TechnologySchool of Information Engineering, Yancheng Institute of TechnologyAbstract In the existing domain adaptation-based bearing fault diagnosis methods, the data difference between the source domain and the target domain is not obvious. Besides, parameters of target domain feature extractor gradually approach that of source domain feature extractor to cheat discriminator which results in similar feature distribution of source domain and target domain. These issues make it difficult for the domain adaptation-based bearing fault diagnosis methods to achieve satisfactory performance. An unsupervised domain adaptive bearing fault diagnosis method based on maximum domain discrepancy (UDA-BFD-MDD) is proposed in this paper. In UDA-BFD-MDD, maximum domain discrepancy is exploited to maximize the feature difference between the source domain and target domain, while the output feature of target domain feature extractor can cheat the discriminator. The performance of UDA-BFD-MDD is verified through comprehensive experiments using the bearing dataset of Case Western Reserve University. The experimental results demonstrate that UDA-BFD-MDD is more stable during training process and can achieve higher accuracy rate.https://doi.org/10.1186/s13634-023-01107-x |
| spellingShingle | Cuixiang Wang Shengkai Wu Xing Shao Unsupervised domain adaptive bearing fault diagnosis based on maximum domain discrepancy EURASIP Journal on Advances in Signal Processing |
| title | Unsupervised domain adaptive bearing fault diagnosis based on maximum domain discrepancy |
| title_full | Unsupervised domain adaptive bearing fault diagnosis based on maximum domain discrepancy |
| title_fullStr | Unsupervised domain adaptive bearing fault diagnosis based on maximum domain discrepancy |
| title_full_unstemmed | Unsupervised domain adaptive bearing fault diagnosis based on maximum domain discrepancy |
| title_short | Unsupervised domain adaptive bearing fault diagnosis based on maximum domain discrepancy |
| title_sort | unsupervised domain adaptive bearing fault diagnosis based on maximum domain discrepancy |
| url | https://doi.org/10.1186/s13634-023-01107-x |
| work_keys_str_mv | AT cuixiangwang unsuperviseddomainadaptivebearingfaultdiagnosisbasedonmaximumdomaindiscrepancy AT shengkaiwu unsuperviseddomainadaptivebearingfaultdiagnosisbasedonmaximumdomaindiscrepancy AT xingshao unsuperviseddomainadaptivebearingfaultdiagnosisbasedonmaximumdomaindiscrepancy |