Fault diagnosis of rolling bearing based on visual spectrum signal feature extraction

Aiming at the problem that the traditional fault feature extraction method of rolling bearing is easy to be disturbed by external noises and contains a lot of redundant information, a fault diagnosis method of feature extraction of visible spectrum signal was proposed. Firstly, the vibration signal is converted into viewable map signal, the adjacency matrix and Laplace matrix of each visual map signal are calculated, and various map indexes are obtained. The appropriate fault features are selected as the fault feature vector by using the double sample Z-value method. Finally, support vector machine (SVM) classification algorithm for bearing fault diagnosis classification results. The experimental analysis shows that, compared with the traditional fault feature extraction method, for different types of fault diagnosis of rolling bearing, the accuracy of fault feature extraction method based on visual map signal is improved by 8.34%; In order to further prove this method, for different degrees of fault diagnosis of the outer ring of rolling bearing, the accuracy of this method is improved by 16.67%, which shows the superiority of the signal feature extraction method based on visual spectrum .