Aircraft Bearing Fault Diagnosis Method Based on LSTM-IDRSN
A fault diagnosis model for aviation bearing is proposed to tackle the challenge of feature extraction from bearing vibration signals amidst noise. This model combines a long short-term memory (LSTM) network with an improved deep residual shrinkage network (IDRSN) based on semi-soft threshold optimi...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10852206/ |
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| author | Lei Wang Kun He Haipeng Fu Weixing Chen |
| author_facet | Lei Wang Kun He Haipeng Fu Weixing Chen |
| author_sort | Lei Wang |
| collection | DOAJ |
| description | A fault diagnosis model for aviation bearing is proposed to tackle the challenge of feature extraction from bearing vibration signals amidst noise. This model combines a long short-term memory (LSTM) network with an improved deep residual shrinkage network (IDRSN) based on semi-soft threshold optimization. The LSTM module first extracts temporal features from the original one-dimensional vibration signals, followed by deep feature extraction via the IDRSN. A fully connected layer with a SoftMax activation function is then used to classify faults in the training set. The model is then validated through different fault test sets. Experimental results show that the LSTM-IDRSN model outperforms the traditional deep residual shrinkage network (DRSN) model, achieving a 4.98% increase in classification accuracy, reaching 96.43%. Additionally, the model maintains high precision and stability even under noise interference, outperforming other methods. |
| format | Article |
| id | doaj-art-fb9d1975501847a99f30b0974bc6b0ba |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-fb9d1975501847a99f30b0974bc6b0ba2025-01-31T00:01:09ZengIEEEIEEE Access2169-35362025-01-0113192481925610.1109/ACCESS.2025.353355110852206Aircraft Bearing Fault Diagnosis Method Based on LSTM-IDRSNLei Wang0https://orcid.org/0009-0009-2429-5935Kun He1https://orcid.org/0009-0003-5503-0346Haipeng Fu2https://orcid.org/0009-0008-6212-1728Weixing Chen3Engineering Techniques Training Center, Civil Aviation University of China, Tianjin, ChinaCollege of Electronic Information and Automation, Civil Aviation University of China, Tianjin, ChinaCollege of Electronic Information and Automation, Civil Aviation University of China, Tianjin, ChinaCollege of Electronic Information and Automation, Civil Aviation University of China, Tianjin, ChinaA fault diagnosis model for aviation bearing is proposed to tackle the challenge of feature extraction from bearing vibration signals amidst noise. This model combines a long short-term memory (LSTM) network with an improved deep residual shrinkage network (IDRSN) based on semi-soft threshold optimization. The LSTM module first extracts temporal features from the original one-dimensional vibration signals, followed by deep feature extraction via the IDRSN. A fully connected layer with a SoftMax activation function is then used to classify faults in the training set. The model is then validated through different fault test sets. Experimental results show that the LSTM-IDRSN model outperforms the traditional deep residual shrinkage network (DRSN) model, achieving a 4.98% increase in classification accuracy, reaching 96.43%. Additionally, the model maintains high precision and stability even under noise interference, outperforming other methods.https://ieeexplore.ieee.org/document/10852206/Bearing fault diagnosisdeep residual shrinkage networklong short-term memory networknoise interference |
| spellingShingle | Lei Wang Kun He Haipeng Fu Weixing Chen Aircraft Bearing Fault Diagnosis Method Based on LSTM-IDRSN IEEE Access Bearing fault diagnosis deep residual shrinkage network long short-term memory network noise interference |
| title | Aircraft Bearing Fault Diagnosis Method Based on LSTM-IDRSN |
| title_full | Aircraft Bearing Fault Diagnosis Method Based on LSTM-IDRSN |
| title_fullStr | Aircraft Bearing Fault Diagnosis Method Based on LSTM-IDRSN |
| title_full_unstemmed | Aircraft Bearing Fault Diagnosis Method Based on LSTM-IDRSN |
| title_short | Aircraft Bearing Fault Diagnosis Method Based on LSTM-IDRSN |
| title_sort | aircraft bearing fault diagnosis method based on lstm idrsn |
| topic | Bearing fault diagnosis deep residual shrinkage network long short-term memory network noise interference |
| url | https://ieeexplore.ieee.org/document/10852206/ |
| work_keys_str_mv | AT leiwang aircraftbearingfaultdiagnosismethodbasedonlstmidrsn AT kunhe aircraftbearingfaultdiagnosismethodbasedonlstmidrsn AT haipengfu aircraftbearingfaultdiagnosismethodbasedonlstmidrsn AT weixingchen aircraftbearingfaultdiagnosismethodbasedonlstmidrsn |