WaveletKernelNet: An Interpretable Deep Neural Network for Industrial Intelligent Diagnosis

TL;DR

WaveletKernelNet is an interpretable deep neural network that uses wavelet convolution to improve mechanical fault diagnosis, achieving higher accuracy with fewer parameters and faster convergence.

Contribution

The paper introduces WaveletKernelNet, a novel wavelet-driven neural network with a specialized convolutional layer for interpretable and effective fault diagnosis.

Findings

WKN outperforms standard CNN in accuracy and convergence speed.

The CWConv layer provides interpretable filters related to physical fault features.

WKN requires fewer parameters than traditional CNNs.

Abstract

Convolutional neural network (CNN), with ability of feature learning and nonlinear mapping, has demonstrated its effectiveness in prognostics and health management (PHM). However, explanation on the physical meaning of a CNN architecture has rarely been studied. In this paper, 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 filters. 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 filter bank, specifically tuned for extracting defect-related impact component embedded in the vibration signal. In addition, three experimental verification using data from laboratory environment are carried out to verify effectiveness of the proposed method for mechanical fault diagnosis. The results show the importance of the designed CWConv layer and the output of CWConv layer is interpretable. Besides, it is found that WKN has fewer parameters, higher fault classification accuracy and faster convergence speed than standard CNN.