Measuring compound defect of bearing by wavelet gradient integrated spiking neural network

Abstract

Compound defect is the common damage of equipment. However, measuring compound defect is difficult because of the complex correlation between multiple single-point defects. Fortunately, as the third generation of neural network that is closest to biological intelligence, the spiking neural network (SNN) has a huge potential on building classifiers. Moreover, time-frequency processing technologies for raw signals are often independent of the training of neural network, so that the end-to-end mode is not able to be built. Therefore, this paper tries to combine the wavelet packet transform (WPT) with SNN, and proposes a wavelet gradient integrated spiking neural network (WGI-SNN) framework. First, a wavelet gradient propagation mechanism (WGPM) is deduced, then recursive down-sampling wavelet networks (DSWNs) is designed to simulate the WPT. Secondly, a SNN model integrating DSWNs is constructed to measure compound defect samples. In addition, an experiment is implemented to verify the effectiveness and practicability of the proposed method.