Two-stage multi-feature fusion method based on self-attention mechanism and its application to mechanical fault diagnosis

Mechanical fault diagnosis plays a vital role in ensuring the safety and reliability of industrial equipment. Existing techniques, however, exhibit significant limitations in face of multi-faults and multi-modal signals. This article presents a two-stage multi-feature fusion strategy employing a self-attention mechanism to enhance fault diagnosis accuracy and robustness. Employing fast Fourier transform (FFT) and wavelet packet transform (WPT), frequency and time-frequency domain features are extracted in the first stage. Convolutional neural networks (CNN) and bidirectional long short-term memory (BiLSTM) networks are utilized to capture spatial and temporal information in the second stage. The features are fused complementarily by self-attention mechanism in the two stages respectively. Experimental results demonstrate an impressive diagnostic accuracy of 99.83% and 99.46% on the public dataset and self-collected dataset, respectively. Extensive comparison experiments verify the superiority of the proposed method compared to some others, validating its effectiveness and application potential in mechanical fault diagnosis.