Neural Network Driven by Multiple Lifting Kernels for Mechanical Fault Diagnosis and Its Interpretability Research of Feature Extraction

doi:10.3901/JME.2024.12.051

Abstract

Abstract: Deep learning method represented by convolutional neural network(CNN) provides an effective tool for big data analysis and processing of mechanical fault diagnosis, but the crack at “black box” issue is one of the important research fields of credible, safe and reliable artificial intelligence and its mechanical fault intelligence diagnosis methods. Lifting multiwavelet framework is a natural multichannel convolution process, and could effectively extract multiple fault features hidden in background noise based on the idea of multiwavelet inner product matching. Therefore, this paper introduces the lifting multiwavelet theory into CNN, proposes the neural network driven by multiple lifting kernels for mechanical fault diagnosis, and discusses the fault feature extraction mechanism for its underlying multichannel convolution. Firstly, the network designs an adaptive lifting multiwavelet layer by lifting multiwavelet framework as the underlying architecture. Multiple lifting kernels are constructed with important signal processing characteristics by the mathematical constraints of lifting multiwavelet theory. Multiple fault feature matching and extraction could be accurately and efficiently achieved by training a single parameter. Secondly, the physical connotation of the network based on the inner product matching principle is studied by simulation experiments. The waveform evolution law of multiple lifting kernels in the training process is discussed. The interpretability problems for feature extraction such as the operation mechanism of multichannel convolution and relationship meaning of network mapping are studied. Finally, the experimental case shows that the method has nice diagnostic accuracy, stability and anti-noise for fault identification of planetary gearboxes with small inter-class differences among the same working conditions and large intra-class differences within multiple working conditions. Meanwhile, the engineering application shows that the method also has accurate diagnosis ability for weak and multiple fault identification of high precision antenna pointing mechanism.

Key words: convolutional neural network, lifting multiwavelets, interpretability, feature extraction

CLC Number:

TH17

YUAN Jing, REN Gangxing, JIANG Huiming, ZHAO Qian, WEI Chenjun, ZHU Jun. Neural Network Driven by Multiple Lifting Kernels for Mechanical Fault Diagnosis and Its Interpretability Research of Feature Extraction[J]. Journal of Mechanical Engineering, 2024, 60(12): 51-64.

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