A Rolling Bearing Fault Diagnosis Method Based on Scaled Dot-Product Attention

Abstract

To tackle the persistent challenges of low bearing fault diagnosis accuracy—specifically the difficulty of extracting faint fault features under complex, variable operating conditions and strong background noise, as well as the tendency to lose deep temporal dependencies—this paper proposes a novel diagnosis method integrating FFT-VMD feature extraction with a Bi-TCN-Bi-GRU neural network. First, to overcome the heuristic parameter selection and frequency-band aliasing inherent in traditional Variational Mode Decomposition (VMD), the Fast Fourier Transform (FFT) is introduced. By extracting global spectral prior information, the FFT guides the VMD to perform adaptive decomposition and effective denoising of non-stationary vibration signals. Subsequently, a dual-path Bi-TCN-Bi-GRU diagnostic model is constructed. The dilated causal convolution mechanism of the Bidirectional Temporal Convolutional Network (Bi-TCN) is utilized to extract deep local spatial features, while the Bidirectional Gated Recurrent Unit (Bi-GRU) is integrated to deeply mine the forward and backward dynamic evolutionary dependencies within the sequence. Experimental results demonstrate that even under severe background noise with a signal-to-noise ratio (SNR) of -2 dB, the proposed method maintains an exceptional diagnostic accuracy of 98.21% on a self-built bearing dataset and 99.90% on the Southeast University (SEU) dataset. This study provides a highly robust and promising solution for enhancing bearing safety and predictive maintenance in complex industrial environments.