Attention on the key modes: Machinery fault diagnosis transformers through variational mode decomposition

Hebin Liu, Qizhi Xu, Xiaolin Han, Biao Wang, Xiaojian Yi

Abstract: Machinery signals typically consist of multiple sub-signals in different frequency bands, while existing Transformer-based fault diagnosis methods often lack attention to key fault frequencies, causing interference in fault diagnosis. Therefore, an innovative Transformer structure for fault diagnosis based on variational mode decomposition (VMD) is proposed. First, to address the difficulty in identifying signal features arising from coupling of multiple frequency bands, a mode encoder based on VMD is proposed to decompose the coupled modes and calculate the key modes. Second, a position encoding method based on central frequency is proposed to address the lack of attention to signal’s frequency in existing position encoding methods. Third, fault characteristic frequency is used to verify the frequency band attention scores, improving the interpretability and reliability of the network in response to the lack of internal interpretability in fault diagnosis methods based on deep learning. Finally, the proposed method was validated on bearing vibration dataset and motor sound dataset. The results showed that the method has high diagnostic accuracy, and could capture the intrinsic modes of different faults.