Fault Diagnosis for Rolling Bearings Under Complex Working Conditions Based on Domain-Conditioned Adaptation
To address the issue of low diagnostic accuracy caused by noise interference and varying rotational speeds in rolling bearings, a fault diagnosis method based on domain-conditioned feature correction is proposed for rolling bearings under complex working conditions. The approach first constructs a m...
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MDPI AG
2024-11-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/12/11/787 |
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| author | Xu Zhang Gaoquan Gu |
| author_facet | Xu Zhang Gaoquan Gu |
| author_sort | Xu Zhang |
| collection | DOAJ |
| description | To address the issue of low diagnostic accuracy caused by noise interference and varying rotational speeds in rolling bearings, a fault diagnosis method based on domain-conditioned feature correction is proposed for rolling bearings under complex working conditions. The approach first constructs a multi-scale self-calibrating convolutional neural network to aggregate input signals across different scales, adaptively establishing long-range spatial and inter-channel dependencies at each spatial location, thereby enhancing feature modeling under noisy conditions. Subsequently, a domain-conditioned adaptation strategy is introduced to dynamically adjust the activation of self-calibrating convolution channels in response to the differences between source and target domain inputs, generating correction terms for target domain features to facilitate effective domain-specific knowledge extraction. The method then aligns source and target domain features by minimizing inter-domain feature distribution discrepancies, explicitly mitigating the distribution variations induced by changes in working conditions. Finally, within a structural risk minimization framework, model parameters are iteratively optimized to achieve minimal distribution discrepancy, resulting in an optimal coefficient matrix for fault diagnosis. Experimental results using variable working condition datasets demonstrate that the proposed method consistently achieves diagnostic accuracies exceeding 95%, substantiating its feasibility and effectiveness. |
| format | Article |
| id | doaj-art-ca166f9772d44cedbe4d47dca70b6c18 |
| institution | OA Journals |
| issn | 2075-1702 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj-art-ca166f9772d44cedbe4d47dca70b6c182025-08-20T01:54:07ZengMDPI AGMachines2075-17022024-11-01121178710.3390/machines12110787Fault Diagnosis for Rolling Bearings Under Complex Working Conditions Based on Domain-Conditioned AdaptationXu Zhang0Gaoquan Gu1Chongqing Tsingshan Industrial, Chongqing 402760, ChinaCollege of Naval Architecture and Ocean Engineering, Naval University of Engineering, Wuhan 430033, ChinaTo address the issue of low diagnostic accuracy caused by noise interference and varying rotational speeds in rolling bearings, a fault diagnosis method based on domain-conditioned feature correction is proposed for rolling bearings under complex working conditions. The approach first constructs a multi-scale self-calibrating convolutional neural network to aggregate input signals across different scales, adaptively establishing long-range spatial and inter-channel dependencies at each spatial location, thereby enhancing feature modeling under noisy conditions. Subsequently, a domain-conditioned adaptation strategy is introduced to dynamically adjust the activation of self-calibrating convolution channels in response to the differences between source and target domain inputs, generating correction terms for target domain features to facilitate effective domain-specific knowledge extraction. The method then aligns source and target domain features by minimizing inter-domain feature distribution discrepancies, explicitly mitigating the distribution variations induced by changes in working conditions. Finally, within a structural risk minimization framework, model parameters are iteratively optimized to achieve minimal distribution discrepancy, resulting in an optimal coefficient matrix for fault diagnosis. Experimental results using variable working condition datasets demonstrate that the proposed method consistently achieves diagnostic accuracies exceeding 95%, substantiating its feasibility and effectiveness.https://www.mdpi.com/2075-1702/12/11/787rolling bearingfault diagnosisnoisedifferent rotational speedsdomain-conditioned adaptation |
| spellingShingle | Xu Zhang Gaoquan Gu Fault Diagnosis for Rolling Bearings Under Complex Working Conditions Based on Domain-Conditioned Adaptation Machines rolling bearing fault diagnosis noise different rotational speeds domain-conditioned adaptation |
| title | Fault Diagnosis for Rolling Bearings Under Complex Working Conditions Based on Domain-Conditioned Adaptation |
| title_full | Fault Diagnosis for Rolling Bearings Under Complex Working Conditions Based on Domain-Conditioned Adaptation |
| title_fullStr | Fault Diagnosis for Rolling Bearings Under Complex Working Conditions Based on Domain-Conditioned Adaptation |
| title_full_unstemmed | Fault Diagnosis for Rolling Bearings Under Complex Working Conditions Based on Domain-Conditioned Adaptation |
| title_short | Fault Diagnosis for Rolling Bearings Under Complex Working Conditions Based on Domain-Conditioned Adaptation |
| title_sort | fault diagnosis for rolling bearings under complex working conditions based on domain conditioned adaptation |
| topic | rolling bearing fault diagnosis noise different rotational speeds domain-conditioned adaptation |
| url | https://www.mdpi.com/2075-1702/12/11/787 |
| work_keys_str_mv | AT xuzhang faultdiagnosisforrollingbearingsundercomplexworkingconditionsbasedondomainconditionedadaptation AT gaoquangu faultdiagnosisforrollingbearingsundercomplexworkingconditionsbasedondomainconditionedadaptation |