Research on Open-Set Recognition Methods for Rolling Bearing Fault Diagnosis
In rolling bearing fault diagnosis, when an unknown fault is present, the Closed-Set Recognition (CSR) method tends to misclassify it as a known fault. To address this issue, an Open-Set Recognition (OSR) framework is proposed for rolling bearing fault diagnosis in this study. The framework is built...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/10/3019 |
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| author | Jia Xu Yan Wang Renyi Xu Hailin Wang Xinzhi Zhou |
| author_facet | Jia Xu Yan Wang Renyi Xu Hailin Wang Xinzhi Zhou |
| author_sort | Jia Xu |
| collection | DOAJ |
| description | In rolling bearing fault diagnosis, when an unknown fault is present, the Closed-Set Recognition (CSR) method tends to misclassify it as a known fault. To address this issue, an Open-Set Recognition (OSR) framework is proposed for rolling bearing fault diagnosis in this study. The framework is built upon a serial multi-scale convolutional prototype learning (SMCPL) network, enhanced with an efficient channel attention (ECA) mechanism to extract the most critical fault features. The extracted features are fed into the Density Peak Clustering (DPC) module, which identifies known and unknown classes based on the computed local densities and relative distances. Finally, validation is performed on the Case Western Reserve University (CWRU) dataset, the Xi’an Jiaotong University rolling bearing accelerated life test dataset (XJTU-SY), and the Paderborn University bearing dataset (PU), Germany, and the framework is comprehensively evaluated in terms of several evaluation metrics, such as normalization accuracy and feature visualization. The experimental results show that SMCPL-ECA-DPC outperforms the comparative methods of SMCPL, CPL, ANEDL, CNN, and OpenMax and has high diagnostic performance in the identification of unknown faults. |
| format | Article |
| id | doaj-art-7c4810e6865648f5ab60f2a96c71ad5a |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-7c4810e6865648f5ab60f2a96c71ad5a2025-08-20T01:56:42ZengMDPI AGSensors1424-82202025-05-012510301910.3390/s25103019Research on Open-Set Recognition Methods for Rolling Bearing Fault DiagnosisJia Xu0Yan Wang1Renyi Xu2Hailin Wang3Xinzhi Zhou4School of Electronic Information, Sichuan University, Chengdu 610065, ChinaSchool of Electronic Information, Sichuan University, Chengdu 610065, ChinaNational Key Laboratory of Science and Technology on Reactor System Design Technology, Nuclear Power Institute of China, Chengdu 610213, ChinaNational Key Laboratory of Science and Technology on Reactor System Design Technology, Nuclear Power Institute of China, Chengdu 610213, ChinaSchool of Electronic Information, Sichuan University, Chengdu 610065, ChinaIn rolling bearing fault diagnosis, when an unknown fault is present, the Closed-Set Recognition (CSR) method tends to misclassify it as a known fault. To address this issue, an Open-Set Recognition (OSR) framework is proposed for rolling bearing fault diagnosis in this study. The framework is built upon a serial multi-scale convolutional prototype learning (SMCPL) network, enhanced with an efficient channel attention (ECA) mechanism to extract the most critical fault features. The extracted features are fed into the Density Peak Clustering (DPC) module, which identifies known and unknown classes based on the computed local densities and relative distances. Finally, validation is performed on the Case Western Reserve University (CWRU) dataset, the Xi’an Jiaotong University rolling bearing accelerated life test dataset (XJTU-SY), and the Paderborn University bearing dataset (PU), Germany, and the framework is comprehensively evaluated in terms of several evaluation metrics, such as normalization accuracy and feature visualization. The experimental results show that SMCPL-ECA-DPC outperforms the comparative methods of SMCPL, CPL, ANEDL, CNN, and OpenMax and has high diagnostic performance in the identification of unknown faults.https://www.mdpi.com/1424-8220/25/10/3019rolling bearingsOpen-Set Recognitionunknown fault diagnosismulti-scale |
| spellingShingle | Jia Xu Yan Wang Renyi Xu Hailin Wang Xinzhi Zhou Research on Open-Set Recognition Methods for Rolling Bearing Fault Diagnosis Sensors rolling bearings Open-Set Recognition unknown fault diagnosis multi-scale |
| title | Research on Open-Set Recognition Methods for Rolling Bearing Fault Diagnosis |
| title_full | Research on Open-Set Recognition Methods for Rolling Bearing Fault Diagnosis |
| title_fullStr | Research on Open-Set Recognition Methods for Rolling Bearing Fault Diagnosis |
| title_full_unstemmed | Research on Open-Set Recognition Methods for Rolling Bearing Fault Diagnosis |
| title_short | Research on Open-Set Recognition Methods for Rolling Bearing Fault Diagnosis |
| title_sort | research on open set recognition methods for rolling bearing fault diagnosis |
| topic | rolling bearings Open-Set Recognition unknown fault diagnosis multi-scale |
| url | https://www.mdpi.com/1424-8220/25/10/3019 |
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