A fault diagnosis method based on feature-level fusion of multi-sensor information for rotating machinery

Traditionally, most fault diagnosis work on rotating machinery is carried out on single sensor datasets. However, the single feature source may suffer from missing or inaccurate features, which is especially sluggish for fault diagnosis tasks under noise interference. Feature-level fusion of multi-sensor information can obtain more comprehensive and abundant feature information, while improving the feature discrimination. Therefore, through feature-level fusion of multi-sensor information, a parallel multi-scale attentional convolutional neural network (PMSACNN) is proposed in this paper to achieve rotating machinery fault diagnosis. A dilated wide convolutional layer is designed to extract the short-time features of signals with noise by performing sparse sampling on them. The multi-scale structure is constructed to capture the diversity feature information of signals, and the feature-level stitching of multi-sensor information is realized by the parallel input mechanism. Feature fusion is achieved by adaptively correcting the importance of different channel features by using channel attention. The global averaging pooling operation is introduced to reduce the number of parameters and improve the efficiency of the model operation. The effectiveness of PMSACNN is verified by using the bearing dataset acquired from the mechanical comprehensive diagnosis simulation platform. The experimental results indicate that the proposed method outperforms the existing methods of this field in terms of fault diagnosis accuracy and noise immunity, which can improve the reliability and safety of rotating machinery.