Non-negative wavelet matrix factorization-based bearing fault intelligent classification method

Abstract

Abstract There are more and more bearing fault types under considering the fault location and degree, and the corresponding fault classification task is becoming increasingly heavy. Raw signals that have not been processed or simply processed are directly input into convolutional neural network (CNN) for classification, resulting in poor classification performance. Aiming at this issue, a time–frequency joint metric feature extraction technique named non-negative wavelet matrix factorization (NWMF) is developed to extract more effective features by comprehensively leveraging the advantages of continuous wavelet transform and non-negative matrix factorization. Based on the NWMF and CNN, an effective intelligent diagnosis framework is constructed to detect bearing fault. In the proposed framework, based on the NWMF, a non-negative basic matrix with smaller size is calculated from the original time–frequency spectrum and it includes bearing fault-related internal core information. In addition, a novel CNN is developed to identify locations and sizes of fault bearing based on the calculated internal core information. For verifying the effectiveness of the proposed framework in handling heavier tasks, the types of bearing faults in the experiments are set up to 15, the results and comparative analysis reveal that the feasibility and superiority of the proposed method are much better than the other traditional machine learning methods and original deep learning methods, such as the support vector machine, random forest and residual neural network.