A New Intelligent Fault Diagnosis Method and Its Application on Bearings

Fault diagnosis is vital in manufacturing system, however, fault diagnosis is divided into three stages: signal preprocessing, feature extraction and fault classification, which destroys the relationship between each stage and causes a part of the loss of fault information. The feature extraction process depends on the experimenter’s experience, and the recognition rate of the shallow diagnostic model does not achieve satisfactory results. In view of this problem, this paper proposes a method, the first step is converting raw signals into two-dimensional (2-D) images, the step can extract the features of the converted 2-D images and eliminate the impact of expert’s experience on the feature extraction process. Next, an intelligent diagnosis algorithm based on convolutional neural network (CNN) is proposed, which can automatically complete the feature extraction and fault identification of the signal. The effectiveness of the method is verified by using bearing data. Test with different sample sizes and noise signals to analyze their impact on diagnostic capabilities. Compared with other mainstream algorithms, this method has a higher recognition rate and can meet the timeliness of fault diagnosis.