Intelligent fault diagnosis based on multi-sensor data fusion and multi scale dual attention enhanced network

Abstract As a significant component of rotating machinery, the health of rolling bearings and gears directly impact the normal operation of rotating machinery. Traditional single-sensor fault diagnosis approaches often fail to extract sufficient fault information, resulting in low diagnostic accuracy in practical engineering applications. Additionally, conventional multi-sensor fusion diagnosis methods exhibit the low robustness in noisy environments. To tackle these challenges, this paper presents a novel fault diagnosis approach derived from multi-sensor data fusion and a multi-scale dual attention enhanced network (MSDF-MSDAENet). Initially, a multi-sensor data fusion (MSDF) strategy is presented. This method reduces the dimensionality of high-dimensional data from multiple sensors to extract embedded low-dimensional effective information, which is then fused into a three-dimensional pixel matrix image. Subsequently, a multi-scale dual attention enhanced network (MSDAENet) is constructed, incorporating both time-frequency information feature extraction modules and position information extraction modules. These enhancements significantly enhance the model's capacity to extract fault features. By dividing the RGB fused images into a well-organized dataset and inputting it into the network for fault diagnosis, the method achieves intelligent diagnosis of samples with different fault types. Tests were carried out on three datasets, and the presented method was compared with various existing methods. The ultimate experimental results demonstrate the efficacy and superiority of the presented method.