1.School of Management Science and Engineering, Anhui University of Technology, Ma’anshan 243032, China;
2.School of Rail Transportation, Soochow University, Suzhou 215131, China
Abstract:Aiming at problems of insufficient feature information obtained by a single sensor and lack of importance distinction of the fault features extracted by the deep convolutional neural network, a fault diagnosis method combining double-connected attention residual network with sensor information fusion was proposed. Firstly, aiming to obtain more comprehensive fault feature information, the vibration signals collected from different locations were converted into multi-channel inputs using a sensor information fusion strategy. Secondly, a double-connected residual network was designed for the fused input to enhance the capability of fault feature extraction, and the channel attention mechanism module was introduced to assign different weights to the output features, so that the features extracted by the model became more discriminative, thereby the fault diagnosis accuracy can be improved. The proposed method was applied to the bearing data set under complex operating conditions. Experimental results show that the proposed method has good classification accuracy under variable working conditions and noise interference.
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