A brain-inspired energy-efficient Wide Spiking Residual Attention Framework for intelligent fault diagnosis

Abstract

Intelligent fault diagnosis functions as a necessary tool to prevent substantial damage to industrial products and enhance system reliability. While Artificial Neural Networks (ANNs) have been extensively studied in this context, they still face substantial challenges in resource consumption, robustness, and generalizability. To overcome these limitations, researchers have developed the third-generation neural network based on brain’s structure, namely the Spiking Neural Network (SNN), which leverages the concept of time steps and spiking signals for enhanced spatiotemporal feature processing and energy efficiency. This paper proposes the Wide Spiking Residual Grouping Attention Framework (WSRGA-FW), which incorporates the advantages of both ANN and SNN. The WSRGA-FW employs Extended Gramian Representation for signal encoding to reduce noise impact, followed by a tailored ANN with wide convolutional kernels, optimized residual structures, and Grouped Perception Generation (GPG) Layers. These augmentations increase the network’s representation and robustness, particularly in noisy environments. The backbone ANN is transformed into an SNN model, allowing deployment in portable and miniaturized devices with improved application prospects. Performance evaluations across various noisy scenarios using bearing fault datasets demonstrate that WSRGA-FW surpasses existing networks. Visualization of firing rates and energy consumption calculation contribute to the interpretability and intrinsic energy efficiency advantages of SNNs.