Multiscale cascade recurrent dilation convolution network for fault diagnosis of rolling bearing under cross-load conditions

Abstract

Recently, deep learning (DL) models based on convolutional neural networks have achieved satisfactory results in rolling bearing fault diagnosis. However, the bearings usually work in variable loading conditions, and their feature distribution could vary with load. The important features cannot be effectively captured in the convolution process using the existing diagnosis models, resulting in poor generalization performance. In this paper, a novel DL model, named multiscale cascade recurrent dilation convolution network, is proposed by introducing the dilated convolution and global average pooling (GAP) layer. Firstly, a new multiscale cascade structure with different convolution kernel sizes is introduced to extract multiscale features contained in the vibration signal. Secondly, a recurrent dilation convolution strategy is designed in each branch of the multiscale cascade structure to extract abundant feature information. Finally, the GAP is employed to reduce redundant feature vectors and output them, while a classifier of multilayer perceptron is used to automatically identify the fault types. The effectiveness of the proposed algorithm is evaluated by two experimental cases. The results show that the proposed method can successfully identify the labels of fault samples under unknown load conditions using the fault samples with labels under existing load conditions. Compared with other methods, this method exhibits excellent robustness and generalization performance for bearing fault diagnosis under cross-load conditions.