Multi-head spatio-temporal attention based parallel GRU architecture: a novel multi-sensor fusion method for mechanical fault diagnosis

• Published in: Measurement science & technology Vol. 35; no. 1; p. 15111
• Main Authors: Li, Yaozong, Luo, Xiong, Xie, Yuhao, Zhao, Wenbing
• Format: Journal Article
• Language: English
• Published: 01.01.2024
• ISSN: 0957-0233; 1361-6501
• DOI: 10.1088/1361-6501/acfe29

Abstract The sensor signals with multiple measuring points and data types not only bring sufficient information, but also put forward more stringent requirements for multi-sensor fusion efficiency and fault feature extraction capability. The redundancy and conflicts in the information of multi-sensor signals often hinder the accurate extraction of crucial fault features. To address this problem, our study proposes an intelligent mechanical fault diagnosis method, which is based on a multi-head spatio-temporal attention mechanism and parallel gated recurrent units (GRUs) architecture. This method utilizes multiple attention heads to model the correlation information in spatial and temporal dimensions, and employs a parallel GRU network for targeted feature extraction. Finally, it combines local features from different attention heads to achieve flexible scheduling of various spatio-temporal attention modes. This novel application and fusion approach of multi-head attention enables accurate identification of the spatio-temporal value differences in the collected multi-sensor signals from multiple perspectives. Experimental results on multiple mechanical fault datasets show that the proposed method performs well in multi-sensor signals based mechanical fault diagnosis tasks and can maintain effectiveness under small samples and imbalanced data conditions.