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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.23887 |
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| _version_ | 1866917972448116736 |
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| author | Qiao, Bowei Wang, Hongwei |
| author_facet | Qiao, Bowei Wang, Hongwei |
| contents | To satisfy the requirements of the end-to-end fault diagnosis of gears, an integrated intelligent method of fault diagnosis for gears using acceleration signals was proposed, which was based on Gabor-based Adaptive Short-Time Fourier Transform (Gabor-ASTFT) and Dual-Tree Complex Wavelet Transform(DTCWT) algorithms, Dilated Residual structure and feature fusion layer, is proposed in this paper. Initially, the raw one-dimensional acceleration signals collected from the gearbox base using vibration sensors undergo pre-segmentation processing. The Gabor-ASTFT and DTCWT are then applied to convert the original one-dimensional time-domain signals into two-dimensional time-frequency representations, facilitating the preliminary extraction of fault features and obtaining weak feature maps.Subsequently, a dual-channel structure is established using deconvolution and dilated convolution to perform upsampling and downsampling on the feature maps, adjusting their sizes accordingly. A feature fusion layer is then constructed to integrate the dual-channel features, enabling multi-scale analysis of the extracted fault features.Finally, a convolutional neural network (CNN) model incorporating a residual structure is developed to conduct deep feature extraction from the fused feature maps. The extracted features are subsequently fed into a Global Average Pooling(GAP) and a classification function for fault classification. Conducting comparative experiments on different datasets, the proposed method is demonstrated to effectively meet the requirements of end-to-end fault diagnosis for gears. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_23887 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An End-to-End Comprehensive Gear Fault Diagnosis Method Based on Multi-Scale Feature-Level Fusion Strategy Qiao, Bowei Wang, Hongwei Machine Learning To satisfy the requirements of the end-to-end fault diagnosis of gears, an integrated intelligent method of fault diagnosis for gears using acceleration signals was proposed, which was based on Gabor-based Adaptive Short-Time Fourier Transform (Gabor-ASTFT) and Dual-Tree Complex Wavelet Transform(DTCWT) algorithms, Dilated Residual structure and feature fusion layer, is proposed in this paper. Initially, the raw one-dimensional acceleration signals collected from the gearbox base using vibration sensors undergo pre-segmentation processing. The Gabor-ASTFT and DTCWT are then applied to convert the original one-dimensional time-domain signals into two-dimensional time-frequency representations, facilitating the preliminary extraction of fault features and obtaining weak feature maps.Subsequently, a dual-channel structure is established using deconvolution and dilated convolution to perform upsampling and downsampling on the feature maps, adjusting their sizes accordingly. A feature fusion layer is then constructed to integrate the dual-channel features, enabling multi-scale analysis of the extracted fault features.Finally, a convolutional neural network (CNN) model incorporating a residual structure is developed to conduct deep feature extraction from the fused feature maps. The extracted features are subsequently fed into a Global Average Pooling(GAP) and a classification function for fault classification. Conducting comparative experiments on different datasets, the proposed method is demonstrated to effectively meet the requirements of end-to-end fault diagnosis for gears. |
| title | An End-to-End Comprehensive Gear Fault Diagnosis Method Based on Multi-Scale Feature-Level Fusion Strategy |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2503.23887 |