STFP-SNN: Spiking Time-Frequency Patching Spiking Neural Network for Enhanced Fault Diagnosis

Abstract

Spiking neural network (SNN) has drawn substantial research focus due to its high biological interpretability, low energy consumption and effectiveness in time-dependent data processing. However, existing methods fail to fully integrate the biological interpretability of SNN with the physical interpretability of time-frequency transform methods in machinery fault diagnosis. To this end, we propose a spiking time-frequency patching (STFP) spiking neural network model, in which a STFP module is designed for time-frequency feature extraction inside the network, and a spiking temporal–spatial attention module is designed to focus on important features of temporal–spatial dimensions. In addition, the spiking residual network used in the model is also improved in neurons. Extensive experimental results on two datasets of rotating machinery parts demonstrate that, the proposed model achieves superior diagnostic performance benefiting from the designed modules, furnishing an end-to-end fault diagnosis method excelling in accuracy, stability, and interpretability.