Rolling Bearing Fault Diagnosis Based on CWT and Two-Stream Convolutional Neural Network

In the era of industrial big data, data-driven rolling bearing fault diagnosis has received widespread attention. However, most of the current methods only extract features from a single dimension such as time domain, frequency domain or time-frequency domain, and this extraction method often leads to the inability to comprehensively extract the information features. Therefore, this paper considers multi-dimensional feature extraction of information, and thus proposes a fault diagnosis model based on two-branch convolutional neural network to improve the comprehensiveness of feature extraction and the accuracy of the classifier. The experimental validation results on the Case Western Reserve University bearing dataset show that the method can effectively improve the fault diagnosis performance, with an average accuracy of 99% on four datasets.