Bearing Fault Diagnosis Based on Gramian Angular Field and Self-Feedback Spiking Neural Networks

Abstract

In fault diagnosis, feature extraction from vibration signals is widely recognized as the most critical step, as it directly influences the accuracy and reliability of the diagnostic outcomes. To address the limited capability of single-view feature extraction in complex vibration signals and the high economic cost associated with multi-source information fusion, this paper proposes a novel fault diagnosis method based on the Gramian angular field (GAF) and a self-feedback spiking neural network (SF-SNN). The method not only streamlines the network architecture but also enhances the biological plausibility of the SNN model. Initially, GAF is employed to transform the one-dimensional vibration signals collected by sensors into two-dimensional images, effectively preserving the temporal dependencies inherent in the signals. Subsequently, conventional spiking neurons are replaced with self-feedback neurons to enable faster and more precise feature recognition, thereby improving diagnostic performance and classification accuracy. This method inherits the low power consumption and high bionic properties of SNNs while enhancing the efficiency, performance, and robustness of SNN models. It achieved high accuracies of 99.92% and 99.77% on the Case Western Reserve University bearing dataset and the Jiangnan University bearing dataset, respectively. Simultaneously, under noisy conditions (signal-to-noise ratio of –4 dB), it attained accuracies of 80.35% and 80.12%, significantly outperforming other methods. These results fully demonstrate the high accuracy and robust performance of the proposed method on bearing fault diagnosis.

Keywords

Gramian angular field (GAF) / spiking neural network / signal processing / spiking self-feedback neuron