End-to-End Continuous/Discontinuous Feature Fusion Method with Attention for Rolling Bearing Fault Diagnosis
Mechanical equipment failure may cause massive economic and even life loss. Therefore, the diagnosis of the failures of machine parts in time is crucial. The rolling bearings are one of the most valuable parts, which have attracted the focus of fault diagnosis. Many successful rolling bearing fault...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/17/6489 |
| _version_ | 1797493169991974912 |
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| author | Jianbo Zheng Jian Liao Zongbin Chen |
| author_facet | Jianbo Zheng Jian Liao Zongbin Chen |
| author_sort | Jianbo Zheng |
| collection | DOAJ |
| description | Mechanical equipment failure may cause massive economic and even life loss. Therefore, the diagnosis of the failures of machine parts in time is crucial. The rolling bearings are one of the most valuable parts, which have attracted the focus of fault diagnosis. Many successful rolling bearing fault diagnoses have been made based on machine learning and deep learning. However, most diagnosis methods still rely on complex signal processing and artificial features, bringing many costs to the deployment and migration of diagnostic models. This paper proposes an end-to-end continuous/discontinuous feature fusion method for rolling bearing fault diagnosis (C/D-FUSA). This method comprises long short-term memory (LSTM), convolutional neural networks (CNN) and attention mechanism, which automatically extracts the continuous and discontinuous features from vibration signals for fault diagnosis. We also propose a contextual-dependent attention module for the LSTM layers. We compare the method with the other simpler deep learning methods and state-of-the-art methods in rolling bearing fault data sets with different sample rates. The results show that our method is more accurate than the other methods with real-time inference. It is also easy to be deployed and trained in a new environment. |
| first_indexed | 2024-03-10T01:16:09Z |
| format | Article |
| id | doaj.art-7a0fdd7bd8e0401fb784616665ba7fb5 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T01:16:09Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-7a0fdd7bd8e0401fb784616665ba7fb52023-11-23T14:09:20ZengMDPI AGSensors1424-82202022-08-012217648910.3390/s22176489End-to-End Continuous/Discontinuous Feature Fusion Method with Attention for Rolling Bearing Fault DiagnosisJianbo Zheng0Jian Liao1Zongbin Chen2Institute of Vibration and Noise, Naval University of Engineering, Wuhan 430033, ChinaInstitute of Vibration and Noise, Naval University of Engineering, Wuhan 430033, ChinaInstitute of Vibration and Noise, Naval University of Engineering, Wuhan 430033, ChinaMechanical equipment failure may cause massive economic and even life loss. Therefore, the diagnosis of the failures of machine parts in time is crucial. The rolling bearings are one of the most valuable parts, which have attracted the focus of fault diagnosis. Many successful rolling bearing fault diagnoses have been made based on machine learning and deep learning. However, most diagnosis methods still rely on complex signal processing and artificial features, bringing many costs to the deployment and migration of diagnostic models. This paper proposes an end-to-end continuous/discontinuous feature fusion method for rolling bearing fault diagnosis (C/D-FUSA). This method comprises long short-term memory (LSTM), convolutional neural networks (CNN) and attention mechanism, which automatically extracts the continuous and discontinuous features from vibration signals for fault diagnosis. We also propose a contextual-dependent attention module for the LSTM layers. We compare the method with the other simpler deep learning methods and state-of-the-art methods in rolling bearing fault data sets with different sample rates. The results show that our method is more accurate than the other methods with real-time inference. It is also easy to be deployed and trained in a new environment.https://www.mdpi.com/1424-8220/22/17/6489fault diagnosisrolling bearingdeep learningLSTMCNNattention |
| spellingShingle | Jianbo Zheng Jian Liao Zongbin Chen End-to-End Continuous/Discontinuous Feature Fusion Method with Attention for Rolling Bearing Fault Diagnosis Sensors fault diagnosis rolling bearing deep learning LSTM CNN attention |
| title | End-to-End Continuous/Discontinuous Feature Fusion Method with Attention for Rolling Bearing Fault Diagnosis |
| title_full | End-to-End Continuous/Discontinuous Feature Fusion Method with Attention for Rolling Bearing Fault Diagnosis |
| title_fullStr | End-to-End Continuous/Discontinuous Feature Fusion Method with Attention for Rolling Bearing Fault Diagnosis |
| title_full_unstemmed | End-to-End Continuous/Discontinuous Feature Fusion Method with Attention for Rolling Bearing Fault Diagnosis |
| title_short | End-to-End Continuous/Discontinuous Feature Fusion Method with Attention for Rolling Bearing Fault Diagnosis |
| title_sort | end to end continuous discontinuous feature fusion method with attention for rolling bearing fault diagnosis |
| topic | fault diagnosis rolling bearing deep learning LSTM CNN attention |
| url | https://www.mdpi.com/1424-8220/22/17/6489 |
| work_keys_str_mv | AT jianbozheng endtoendcontinuousdiscontinuousfeaturefusionmethodwithattentionforrollingbearingfaultdiagnosis AT jianliao endtoendcontinuousdiscontinuousfeaturefusionmethodwithattentionforrollingbearingfaultdiagnosis AT zongbinchen endtoendcontinuousdiscontinuousfeaturefusionmethodwithattentionforrollingbearingfaultdiagnosis |