Bearing fault feature selection method based on dynamic time warped related searches

Traditional feature selection algorithms rarely consider the dynamic misalignment between different time series, and have poor fault tolerance and robustness. In this paper, a fault feature selection method for rolling bearings based on Dynamic Time Warped Related Searches (DTWRS) is proposed. Firstly, the bearing fault feature set is constructed, and the dynamic time warping algorithm is used to calculate the shortest cumulative distance between feature of different faults, and this distance is used as the correlation evaluation standard. Then, two new search rules, dynamic time warping difference and dynamic time warping entropy, are proposed based on the minimum redundancy between bearing fault features and the maximum correlation between fault features and feature categories, use these two search rules to judge the ability of the feature to express the fault, define the quality of the fault feature and sort from good to bad according to the level of ability. Finally, in this order, the number of features is gradually increased and input to the fault classifier, and the sensitive fault feature set is obtained based on the principle of the highest recognition rate and the least number of features. The experimental results show that the fault feature selection method of rolling bearing based on DTWRS can increase the accuracy of fault diagnosis while minimizing the number of features, and improve the efficiency and effect of fault diagnosis.