Bearing Fault Diagnosis under Transient Conditions: Using Variational Mode Decomposition and the Symmetrized Dot Pattern-Based Convolutional Neural Network Model

An effective bearing fault diagnosis method for gearbox applications under variable operating conditions is proposed, utilizing variational mode decomposition (VMD) for feature extraction, symmetrized dot pattern (SDP) for visual representation, and convolutional neural network (CNN) for deep feature extraction and classification. First, three operating conditions of normal bearing, bearing outer ring fault, and bearing inner ring fault were simulated. The vibration and speed pulse signals under variable conditions were collected synchronously, and the nonstationary time-domain vibration signals were converted into stationary angular-domain vibration signals by the full-period angular-domain resampling technique. To eliminate the noise components, VMD was used to decompose the vibration signal into different frequency bands to obtain the intrinsic mode functions (IMFs), which were further represented by SDP images, and feature images were extracted according to the noncorrelation coefficient. Finally, based on the designed CNN model, the depth features of the SDP image were automatically extracted to form the feature vector, and the formed feature vector was used as the input to the softmax classifier to identify the bearing fault condition. Experimental results showed that the proposed method achieved the diagnostic accuracy of 100%. Compared with other SDP-based methods, this method has good feature extraction and high fault diagnosis accuracy.