Biologically inspired compound defect detection using a spiking neural network with continuous time–frequency gradients

Abstract

Compound defects frequently arise, posing serious threats to the reliability and safety of machines as their structures become increasingly complex. Traditional approaches primarily rely on deep learning algorithms based on artificial neural networks. However, these methods cannot fully replicate the information transmission functions of human brain neurons, resulting in limited biological interpretability and reduced reliability in practice. To address this challenge, this paper introduces a biologically inspired approach for compound defect detection using a spiking neural network with an improved pooling layer. The proposed method integrates a specially designed spiking neuron with a wavelet packet pooling mechanism (WPPM), forming the WPPM-spiking neural network (WPPM-SNN) model. This model employs spiking layers enhanced by a wavelet gradient, enabling it to adeptly extract nuanced features from preprocessed samples with compound defects. Specifically, WPPM simulates the wavelet transform within the pooling layers, based on mathematical analysis. This model was studied on both laboratorial and engineering verifications, achieving composite defect accuracies of 99% and 84.79%, respectively. Compared with popular deep models, the proposed model demonstrated accuracy improvements of 6.92% and 5.87%, respectively. Those empirical results on multiple evaluation metrics clearly demonstrate that the WPPM-SNN model significantly outperforms popular multi-label learning techniques in detecting compound defects.