Fault Diagnosis of Rolling Element Bearing Based on BiTCN-Attention and OCSSA Mechanism
This paper proposes a novel fault diagnosis framework that integrates the Osprey–Cauchy–Sparrow Search Algorithm (OCSSA) optimized Variational Mode Decomposition (VMD) with a Bidirectional Temporal Convolutional Network-Attention mechanism (BiTCN-Attention). To address the limitations of empirical p...
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| Language: | English |
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MDPI AG
2025-04-01
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| Online Access: | https://www.mdpi.com/2076-0825/14/5/218 |
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| author | Yuchen Yang Chunsong Han Guangtao Ran Tengyu Ma Juntao Pan |
| author_facet | Yuchen Yang Chunsong Han Guangtao Ran Tengyu Ma Juntao Pan |
| author_sort | Yuchen Yang |
| collection | DOAJ |
| description | This paper proposes a novel fault diagnosis framework that integrates the Osprey–Cauchy–Sparrow Search Algorithm (OCSSA) optimized Variational Mode Decomposition (VMD) with a Bidirectional Temporal Convolutional Network-Attention mechanism (BiTCN-Attention). To address the limitations of empirical parameter selection in VMD, OCSSA adaptively optimizes the decomposition parameters (penalty factor <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>α</mi></semantics></math></inline-formula> and mode number <i>K</i>) through a hybrid strategy that combines chaotic initialization, Osprey-inspired global search, and Cauchy mutation. Subsequently, the BiTCN captures bidirectional temporal dependencies from vibration signals, while the attention mechanism dynamically filters critical fault features, constructing an end-to-end diagnostic model. Experiments on the CWRU dataset demonstrate that the proposed method achieves an average accuracy of 99.44% across 10 fault categories, outperforming state-of-the-art models (e.g., VMD-TCN: 97.5%, CNN-BiLSTM: 84.72%). |
| format | Article |
| id | doaj-art-092e7a2ab2c14cecbbdf5a0e840412a6 |
| institution | OA Journals |
| issn | 2076-0825 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Actuators |
| spelling | doaj-art-092e7a2ab2c14cecbbdf5a0e840412a62025-08-20T01:56:57ZengMDPI AGActuators2076-08252025-04-0114521810.3390/act14050218Fault Diagnosis of Rolling Element Bearing Based on BiTCN-Attention and OCSSA MechanismYuchen Yang0Chunsong Han1Guangtao Ran2Tengyu Ma3Juntao Pan4School of Mechatronics Engineering, Qiqihar University, Qiqihar 161006, ChinaSchool of Mechatronics Engineering, Qiqihar University, Qiqihar 161006, ChinaDepartment of Control Science and Engineering, Harbin Institute of Technology, Harbin 150001, ChinaSchool of Science, Qiqihar University, Qiqihar 161006, ChinaSchool of Electrical and Information Engineering, North Minzu University, Yinchuan 750030, ChinaThis paper proposes a novel fault diagnosis framework that integrates the Osprey–Cauchy–Sparrow Search Algorithm (OCSSA) optimized Variational Mode Decomposition (VMD) with a Bidirectional Temporal Convolutional Network-Attention mechanism (BiTCN-Attention). To address the limitations of empirical parameter selection in VMD, OCSSA adaptively optimizes the decomposition parameters (penalty factor <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>α</mi></semantics></math></inline-formula> and mode number <i>K</i>) through a hybrid strategy that combines chaotic initialization, Osprey-inspired global search, and Cauchy mutation. Subsequently, the BiTCN captures bidirectional temporal dependencies from vibration signals, while the attention mechanism dynamically filters critical fault features, constructing an end-to-end diagnostic model. Experiments on the CWRU dataset demonstrate that the proposed method achieves an average accuracy of 99.44% across 10 fault categories, outperforming state-of-the-art models (e.g., VMD-TCN: 97.5%, CNN-BiLSTM: 84.72%).https://www.mdpi.com/2076-0825/14/5/218bearings fault diagnosisbidirectional temporal convolutional network-attention mechanism (BiTCN-Attention)variational mode decomposition (VMD)Osprey–Cauchy–Sparrow search algorithm (OCSSA) |
| spellingShingle | Yuchen Yang Chunsong Han Guangtao Ran Tengyu Ma Juntao Pan Fault Diagnosis of Rolling Element Bearing Based on BiTCN-Attention and OCSSA Mechanism Actuators bearings fault diagnosis bidirectional temporal convolutional network-attention mechanism (BiTCN-Attention) variational mode decomposition (VMD) Osprey–Cauchy–Sparrow search algorithm (OCSSA) |
| title | Fault Diagnosis of Rolling Element Bearing Based on BiTCN-Attention and OCSSA Mechanism |
| title_full | Fault Diagnosis of Rolling Element Bearing Based on BiTCN-Attention and OCSSA Mechanism |
| title_fullStr | Fault Diagnosis of Rolling Element Bearing Based on BiTCN-Attention and OCSSA Mechanism |
| title_full_unstemmed | Fault Diagnosis of Rolling Element Bearing Based on BiTCN-Attention and OCSSA Mechanism |
| title_short | Fault Diagnosis of Rolling Element Bearing Based on BiTCN-Attention and OCSSA Mechanism |
| title_sort | fault diagnosis of rolling element bearing based on bitcn attention and ocssa mechanism |
| topic | bearings fault diagnosis bidirectional temporal convolutional network-attention mechanism (BiTCN-Attention) variational mode decomposition (VMD) Osprey–Cauchy–Sparrow search algorithm (OCSSA) |
| url | https://www.mdpi.com/2076-0825/14/5/218 |
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