WaveletKernelNet: An Interpretable Deep Neural Network for Industrial Intelligent Diagnosis

By Tianfu Li, Zhibin Zhao, Chuang Sun, Li Cheng, Xuefeng Chen, Ruqaing Yan, Robert X. Gao. IEEE Transactions on Systems, Man & Cybernetics. Systems, 2019

515 citations recorded by the source corpus.

Convolutional neural network (CNN), with the ability of feature learning and nonlinear mapping, has demonstrated its effectiveness in prognostics and health management (PHM). However, an explanation on the physical meaning of a CNN architecture has rarely been studied. In this article, a novel wavelet-driven deep neural network, termed as WaveletKernelNet (WKN), is presented, where a continuous wavelet convolutional (CWConv) layer is designed to replace the first convolutional layer of the standard CNN. This enables the first CWConv layer to discover more meaningful kernels. Furthermore, only the scale parameter and translation parameter are directly learned from raw data at this CWConv layer. This provides a very effective way to obtain a customized kernel bank, specifically tuned for extracting defect-related impact component embedded in the vibration signal. In addition, three experimental studies using data from laboratory environment are carried out to verify the effectiveness of the proposed method for mechanical fault diagnosis. The experimental results show that the accuracy of the WKNs is higher than CNN by more than 10%, which indicate the importance of the designed CWConv layer. Besides, through theoretical analysis and feature map visualization, it is found that the WKNs are interpretable, have fewer parameters, and have the ability to converge faster within the same training epochs.

Subfields: Control and Systems Engineering, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/3caaf6e3e86a3c8354ad8d1a4045ad2890a67ab1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Chuang Sun
2. Xuefeng Chen
3. Ruqiang Yan
4. Miguel López-Benítez
5. Shibin Wang
6. Fei Ma
7. Enggee Lim
8. Tiantian Guo

In Industrial and Manufacturing Engineering

1. Ruqiang Yan

Citations in the corpus

• A Review of Wavelet Analysis and Its Applications: Challenges and Opportunities (2022, methodology): “It provides a very efficient way to obtain custom filter banks [127].”
• Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark Study (2020, background): “Actually, some papers in intelligent fault diagnosis have noted this problem and attempted to propose some interpretable model [60, 61].”