Rolling Bearing Fault Diagnosis Based on Recurrence Plot
A bearing fault diagnosis method based on recurrence plots and a fusion neural network is proposed to address the low recognition accuracy of noisy bearing vibration data. Compared to existing methods, this approach leverages the recurrence plot technique to convert vibration signals into color imag...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10703061/ |
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| author | Zheming Chen Bin Xu Zhong Zhang |
| author_facet | Zheming Chen Bin Xu Zhong Zhang |
| author_sort | Zheming Chen |
| collection | DOAJ |
| description | A bearing fault diagnosis method based on recurrence plots and a fusion neural network is proposed to address the low recognition accuracy of noisy bearing vibration data. Compared to existing methods, this approach leverages the recurrence plot technique to convert vibration signals into color images, which carry more information than grayscale images. For the prediction model, the traditional convolutional neural network is enhanced by integrating bidirectional gated recurrent unit and multi-head attention mechanism, allowing it to capture temporal features alongside the spatial features typically extracted by convolutional neural network. The accuracy of the method exceeds 92% on two different bearing datasets, indicating its strong generalization performance. The results of ablation and comparison experiments demonstrate that the proposed model achieves high prediction accuracy even in the presence of strong noise, exhibiting robust noise immunity compared with other methods. |
| format | Article |
| id | doaj-art-f54d3e3a75ac42e29c5e4c759d097de0 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-f54d3e3a75ac42e29c5e4c759d097de02025-08-20T01:47:50ZengIEEEIEEE Access2169-35362024-01-011214971014972110.1109/ACCESS.2024.347245410703061Rolling Bearing Fault Diagnosis Based on Recurrence PlotZheming Chen0Bin Xu1https://orcid.org/0009-0002-5866-1007Zhong Zhang2School of Vehicle Engineering, Chongqing University of Technology, Chongqing, ChinaSchool of Vehicle Engineering, Chongqing University of Technology, Chongqing, ChinaSchool of Vehicle Engineering, Chongqing University of Technology, Chongqing, ChinaA bearing fault diagnosis method based on recurrence plots and a fusion neural network is proposed to address the low recognition accuracy of noisy bearing vibration data. Compared to existing methods, this approach leverages the recurrence plot technique to convert vibration signals into color images, which carry more information than grayscale images. For the prediction model, the traditional convolutional neural network is enhanced by integrating bidirectional gated recurrent unit and multi-head attention mechanism, allowing it to capture temporal features alongside the spatial features typically extracted by convolutional neural network. The accuracy of the method exceeds 92% on two different bearing datasets, indicating its strong generalization performance. The results of ablation and comparison experiments demonstrate that the proposed model achieves high prediction accuracy even in the presence of strong noise, exhibiting robust noise immunity compared with other methods.https://ieeexplore.ieee.org/document/10703061/Bearing fault diagnosisconvolutional neural networkconvolutional layersfault diagnosis accuracyfault feature extractionmax-pooling layer |
| spellingShingle | Zheming Chen Bin Xu Zhong Zhang Rolling Bearing Fault Diagnosis Based on Recurrence Plot IEEE Access Bearing fault diagnosis convolutional neural network convolutional layers fault diagnosis accuracy fault feature extraction max-pooling layer |
| title | Rolling Bearing Fault Diagnosis Based on Recurrence Plot |
| title_full | Rolling Bearing Fault Diagnosis Based on Recurrence Plot |
| title_fullStr | Rolling Bearing Fault Diagnosis Based on Recurrence Plot |
| title_full_unstemmed | Rolling Bearing Fault Diagnosis Based on Recurrence Plot |
| title_short | Rolling Bearing Fault Diagnosis Based on Recurrence Plot |
| title_sort | rolling bearing fault diagnosis based on recurrence plot |
| topic | Bearing fault diagnosis convolutional neural network convolutional layers fault diagnosis accuracy fault feature extraction max-pooling layer |
| url | https://ieeexplore.ieee.org/document/10703061/ |
| work_keys_str_mv | AT zhemingchen rollingbearingfaultdiagnosisbasedonrecurrenceplot AT binxu rollingbearingfaultdiagnosisbasedonrecurrenceplot AT zhongzhang rollingbearingfaultdiagnosisbasedonrecurrenceplot |