Research on bearing fault diagnosis based on the stacking strategy

The Stacking-RF model is proposed to improve the low accuracy of a single classifier in bearing fault diagnosis tasks by using a stacking ensemble learning strategy. The model utilizes KNN, LR, SVM, BP, and RF as base classifiers, with RF employed as meta classifiers. Subsequently, we evaluate the performance between the single and stacking models, while investigating the most effective stacking combination to identify various failure modes of rolling bearings accurately. The experimental results of the XJTU-SY bearing dataset show that the diagnosis accuracy of stacking models (98.10%-99.55%) is significantly improved, compared with each member classifier (94.60%-98.98%). It can be demonstrated that the proposed Stacking-RF model can effectively integrate the valuable information of different classifiers, which ultimately leads to a higher accuracy (99.55%). This study shows that the stacking ensemble learning method has a good application prospect in rolling bearing fault diagnosis.