Uncertainty-driven dynamic ensemble framework for rotating machinery fault diagnosis under time-varying working conditions

Abstract

Multi-scale ensemble learning combines different scales of feature resolution, thereby improving fault diagnostic accuracy. However, the effectiveness of different information scales in characterizing fault features under time-varying speed conditions varies with speed. It is difficult for existing ensemble strategies to ensure the effectiveness of feature information when ensemble multi-scale feature information is involved. Accordingly, we propose an uncertainty-driven dynamic ensemble Bayesian convolutional neural network (DEBCNN) framework. The uncertainty of the results of different scale models was used to dynamically determine their weights in the ensemble framework, which reduced the influence of irrelevant features on the diagnostic results. By employing the proposed dynamic ensemble strategy, the ensemble framework can utilize fault feature information corresponding to different rotational speeds in the final diagnostic results. Experiments on motor and bearing datasets illustrate the superiority of this strategy over other techniques. This study provides useful insights for further research in the field of fault diagnosis of rotating machinery at time-varying speeds.