Hierarchical Adaptive Wavelet-Guided Adversarial Network with Physics-Informed Regularization for Generating Multiscale Vibration Signals for Deep Learning-Based Fault Diagnosis of Rotating Machines
Rotating machines predominantly operate under healthy conditions, leading to a limited availability of fault data and a significant class imbalance in diagnostic datasets. These challenges hinder the development and deployment of fault diagnosis methods based on deep learning in practice. Considerin...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
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| Series: | Automation |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4052/6/2/14 |
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| Summary: | Rotating machines predominantly operate under healthy conditions, leading to a limited availability of fault data and a significant class imbalance in diagnostic datasets. These challenges hinder the development and deployment of fault diagnosis methods based on deep learning in practice. Considering these issues, a novel hierarchical adaptive wavelet-guided adversarial network with physics-informed regularization (HAWAN-PIR) is proposed. First, a hierarchical wavelet-based imbalance severity score is used to quantify the data imbalance within the datasets. Second, HAWAN-PIR generates synthetic fault data in the time domain via multiscale wavelet decomposition and represents the first attempt to embed physics-informed regularization to incorporate relevant fault knowledge. The quality of the synthetic fault data is then evaluated via a comprehensive multiscale synthesis quality index. Furthermore, a scale-aware dynamic mixing algorithm is proposed to optimally integrate synthetic data with real data. Finally, a one-dimensional convolutional neural network (1-D CNN) is employed for extracting features and classifying faults. The effectiveness of the proposed method is validated through two case studies: motor bearings and planetary gearboxes. The results show that HAWAN-PIR can synthesize high-quality fake data and improve the diagnostic accuracy of the 1-D CNN by 17% for the bearing case and 15% for the gearbox case. |
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| ISSN: | 2673-4052 |