An interpretable convolutional neural network with multi-wavelet kernel fusion for intelligent fault diagnosis

Abstract

Deep learning (DL) has been widely developed and applied in gearbox fault diagnosis of smart manufacturing systems due to its powerful feature representation ability. However, typical DL-based fault diagnosis approaches do not correlate the domain information or mechanism knowledge into the neural network learning process. This causes issues for the reliability and stability of fault diagnosis engineering applications. To address this problem, we propose a novel interpretable DL model named Multi-Wavelet Kernel Convolution Neural Network (MWKCNN) for fault diagnosis in this article. First, a feature extraction layer named the Multi-Wavelet Kernel Convolution (MWKC) layer is constructed based on the continuous wavelet transform (CWT) to locate and detect the impulse signatures of faults. It can capture sufficient feature representations by involving signal processing knowledge. Then, to balance the influence of different wavelet kernels, we design a kernel weights recalibration (KWR) module to dynamically assign different weights to different wavelet kernels. Finally, the performance of the proposed method is validated using two gearbox datasets. In addition, we illustrate the interpretability of the proposed method by analyzing the peculiarity of different wavelet kernels, the variation of kernel weights, and the visualization of learned features.