Comprehensive Exploitation of Time- and Frequency-Domain Information for Bearing Fault Diagnosis on Imbalanced Datasets via Adaptive Wavelet-like Transform General Adversarial Network and Ensemble Learning
The vibration signals of faulty bearings contain rich feature information in both the time and frequency domains. Effectively leveraging this information is crucial, especially when addressing imbalanced bearing fault datasets, as it can significantly enhance the performance of fault diagnosis model...
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MDPI AG
2025-04-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/7/2328 |
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| author | Huachao Jiao Wenlei Sun Hongwei Wang Xiaojing Wan |
| author_facet | Huachao Jiao Wenlei Sun Hongwei Wang Xiaojing Wan |
| author_sort | Huachao Jiao |
| collection | DOAJ |
| description | The vibration signals of faulty bearings contain rich feature information in both the time and frequency domains. Effectively leveraging this information is crucial, especially when addressing imbalanced bearing fault datasets, as it can significantly enhance the performance of fault diagnosis models. However, existing GAN models and diagnostic methods do not fully exploit these domain-specific features. To overcome this limitation, a novel fault diagnosis method is proposed, based on the Adaptive Wavelet-Like Transform Generative Adversarial Network (AWLT-GAN) and ensemble learning. In the first stage, AWLT-GAN is used to balance the bearing fault dataset by integrating time- and frequency-domain feature information. AWLT-GAN embeds an adaptive wavelet-like transform neural network into the generator as an adaptive layer and employs a dual-discriminator architecture. This design allows the network to simultaneously learn fault characteristics from both domains within a single training session, enhancing the quality of the synthetic fault data. Next, an ensemble learning approach is applied, combining time- and frequency-domain models, with the final classification determined through a soft voting mechanism. Experimental results demonstrate that the vibration signals generated by AWLT-GAN effectively replicate the feature distribution of real data, confirming its high performance. The fault diagnosis model, developed using these high-quality synthetic samples, accurately captures fault characteristics embedded in both the time and frequency domains, resulting in enhanced diagnostic performance. The proposed approach not only addresses the imbalance in bearing fault datasets but also significantly improves diagnostic accuracy. |
| format | Article |
| id | doaj-art-9ad4e64e50104d80ac4b88d6f6d2301e |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-9ad4e64e50104d80ac4b88d6f6d2301e2025-08-20T02:09:14ZengMDPI AGSensors1424-82202025-04-01257232810.3390/s25072328Comprehensive Exploitation of Time- and Frequency-Domain Information for Bearing Fault Diagnosis on Imbalanced Datasets via Adaptive Wavelet-like Transform General Adversarial Network and Ensemble LearningHuachao Jiao0Wenlei Sun1Hongwei Wang2Xiaojing Wan3Intelligent Manufacturing Modern Industry College, Xinjiang University, Urumqi 830049, ChinaIntelligent Manufacturing Modern Industry College, Xinjiang University, Urumqi 830049, ChinaIntelligent Manufacturing Modern Industry College, Xinjiang University, Urumqi 830049, ChinaIntelligent Manufacturing Modern Industry College, Xinjiang University, Urumqi 830049, ChinaThe vibration signals of faulty bearings contain rich feature information in both the time and frequency domains. Effectively leveraging this information is crucial, especially when addressing imbalanced bearing fault datasets, as it can significantly enhance the performance of fault diagnosis models. However, existing GAN models and diagnostic methods do not fully exploit these domain-specific features. To overcome this limitation, a novel fault diagnosis method is proposed, based on the Adaptive Wavelet-Like Transform Generative Adversarial Network (AWLT-GAN) and ensemble learning. In the first stage, AWLT-GAN is used to balance the bearing fault dataset by integrating time- and frequency-domain feature information. AWLT-GAN embeds an adaptive wavelet-like transform neural network into the generator as an adaptive layer and employs a dual-discriminator architecture. This design allows the network to simultaneously learn fault characteristics from both domains within a single training session, enhancing the quality of the synthetic fault data. Next, an ensemble learning approach is applied, combining time- and frequency-domain models, with the final classification determined through a soft voting mechanism. Experimental results demonstrate that the vibration signals generated by AWLT-GAN effectively replicate the feature distribution of real data, confirming its high performance. The fault diagnosis model, developed using these high-quality synthetic samples, accurately captures fault characteristics embedded in both the time and frequency domains, resulting in enhanced diagnostic performance. The proposed approach not only addresses the imbalance in bearing fault datasets but also significantly improves diagnostic accuracy.https://www.mdpi.com/1424-8220/25/7/2328adaptive wavelet-like transformGANbearing fault diagnosisimbalance dataensemble learning |
| spellingShingle | Huachao Jiao Wenlei Sun Hongwei Wang Xiaojing Wan Comprehensive Exploitation of Time- and Frequency-Domain Information for Bearing Fault Diagnosis on Imbalanced Datasets via Adaptive Wavelet-like Transform General Adversarial Network and Ensemble Learning Sensors adaptive wavelet-like transform GAN bearing fault diagnosis imbalance data ensemble learning |
| title | Comprehensive Exploitation of Time- and Frequency-Domain Information for Bearing Fault Diagnosis on Imbalanced Datasets via Adaptive Wavelet-like Transform General Adversarial Network and Ensemble Learning |
| title_full | Comprehensive Exploitation of Time- and Frequency-Domain Information for Bearing Fault Diagnosis on Imbalanced Datasets via Adaptive Wavelet-like Transform General Adversarial Network and Ensemble Learning |
| title_fullStr | Comprehensive Exploitation of Time- and Frequency-Domain Information for Bearing Fault Diagnosis on Imbalanced Datasets via Adaptive Wavelet-like Transform General Adversarial Network and Ensemble Learning |
| title_full_unstemmed | Comprehensive Exploitation of Time- and Frequency-Domain Information for Bearing Fault Diagnosis on Imbalanced Datasets via Adaptive Wavelet-like Transform General Adversarial Network and Ensemble Learning |
| title_short | Comprehensive Exploitation of Time- and Frequency-Domain Information for Bearing Fault Diagnosis on Imbalanced Datasets via Adaptive Wavelet-like Transform General Adversarial Network and Ensemble Learning |
| title_sort | comprehensive exploitation of time and frequency domain information for bearing fault diagnosis on imbalanced datasets via adaptive wavelet like transform general adversarial network and ensemble learning |
| topic | adaptive wavelet-like transform GAN bearing fault diagnosis imbalance data ensemble learning |
| url | https://www.mdpi.com/1424-8220/25/7/2328 |
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