Rolling Bearing Fault Diagnosis Based on Depth-Wise Separable Convolutions with Multi-Sensor Data Weighted Fusion

Abstract

Given the problems of low accuracy and complex process steps currently faced by the field of fault diagnosis, a fault diagnosis method based on multi-sensor data weighted fusion (MSDWF) combined with depth-wise separable convolutions (DWSC) is proposed. The method takes into account the temporal and spatial information contained in multi-sensor data and can realize end-to-end bearing fault diagnosis. MSDWF is committed to comprehensively characterizing the state information of bearings, and the weighted operation of the multi-sensor data is to establish the interactive information to tap into the inline relationship in the data; DWSC equipped with residual connection is used to realize the decoupling of the channel and spatial correlation of the data. In order to verify the proposed method, the data obtained by a different number of sensors with weighted and unweighted states are used as the input of DWSC, respectively, for comparison, and finally, the effectiveness of MSDWF is verified. Through the comparison between different fault diagnosis methods, the method based on MSDWF and DWSC shows better stability and higher accuracy. Finally, when facing different experimental datasets, the method has similar performance, which shows the stability of the method on different datasets.

Keywords

• bearing fault diagnosis
• depth-wise separable convolutions
• multi-sensor data fusion
• data weighting