MSFormer: Multi-Scale Transformer for Robust Fault Diagnosis of Machines Under Complex Conditions

Abstract

In rotating machinery monitoring, obtaining highly discriminative fault features from complex vibration signals remains a significant challenge for deep learning-based diagnostic models. In this paper, a novel intelligent fault diagnosis method named MSFormer is proposed. The MSFormer incorporates a parallel multi-scale Convolutional Neural Network (CNN) architecture and hierarchical Transformer modules to comprehensively process 1D vibration signals. By utilizing varying kernel sizes, the multi-scale CNN extracts both high-frequency local transient impulses and low-frequency global degradation trends. Subsequently, the Transformer modules are employed to model the long-range dependencies within the extracted feature sequences, effectively mitigating the interference of environmental noise. Extensive experiments are conducted on bearing fault experimental data to evaluate the proposed method. Four state-of-the-art models are compared under the same experimental settings. Quantitative metrics and qualitative tools are utilized for comprehensive evaluation. Experimental results indicate that MSFormer achieves a 92.67% accuracy, 92.54% F1-score, and 93.22% precision, demonstrating significant superiority. MSFormer provides a powerful and precise intelligent solution for mechanical fault diagnosis.