Ensemble of Simplified Graph Wavelet Neural Networks for Planetary Gearbox Fault Diagnosis

• Published in: IEEE transactions on instrumentation and measurement Vol. 72; p. 1
• Main Authors: Jiao, Chenyang, Zhang, Dingcheng, Fang, Xia, Miao, Qiang
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks; Chebyshev approximation; Convolution; Effectiveness; Ensemble learning; Fault detection; Fault diagnosis; Feature extraction; Gearboxes; Graph neural networks; graph wavelet neural network; Kernel; Laplace equations; Machine learning; Neural networks; planetary gearbox; small samples; Transforms; Vibration; Wavelet transforms; Wind turbines
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2023.3310092

As an important component of the transmission system, planetary gearboxes are widely used in equipment such as aircraft, wind turbines etc. The changing operating condition results in gear failure easily. The fault diagnosis method using vibration signals can be used to detect gear faults and then avoid accidents. However, there are small faulty samples in the real industrial scenarios, which limits the effectiveness of faults diagnosis methods based on traditional deep learning algorithms. In this paper, an ensemble of novel simplified graph wavelet neural network is proposed for fault diagnosis of planetary gearboxes. In the proposed method, the design of graph wavelet neural networks is simplified to make it more concise and appropriate for fault diagnosis. Different wavelet bases are used to build simplified graph neural networks for extracting diverse features of one sample. Also, a learnable weighting ensemble strategy is proposed to fuse the extracted features. In order to verify the effectiveness of the proposed method, vibration signals of different types of prefabricated faulty gears were acquired in the laboratory and the proposed method was applied for fault diagnosis. The experimental results show that the proposed method has a higher accuracy and a robust comparing with traditional convolutional neural networks and some other graph neural networks such as ChebyNet, GCN and GWNN.