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

Abstract 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 (SAM) 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 SAM 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.