Rolling Bearing Fault Diagnosis Based On Convolutional Capsule Network

Abstract

Fault diagnosis technology has been widely applied and is an important part of ensuring the safe operation of mechanical equipment. In response to the problem of frequent faults in rolling bearings, this paper designs a rolling bearing fault diagnosis method based on Convolutional Capsule Network (CCN). More specifically, the original vibration signal is converted into a two-dimensional time-frequency image using continuous wavelet transform (CWT), and the feature extraction is performed on the two-dimensional time-frequency image using the convolution layer at the front end of the network, and the extracted features are input into the capsule network. The capsule network converts the extracted features into vector neurons, and the dynamic routing algorithm is used to achieve feature transfer and output the results of fault diagnosis. Two different datasets are used to compare with other traditional deep learning models to verify the fault diagnosis capability of the method. The results show that the convolutional capsule network has good diagnostic capability under different working conditions, even in the presence of noise and insufficient samples, compared to other models. This method contributes to the safe and reliable operation of mechanical equipment and is suitable for other rotating scenarios.