A comparative study of rolling bearing fault diagnosis based on tree models

We systematically carried out a comparative study of 12 kinds of tree-based models for the task of rolling bearing fault diagnosis, using the publicly available XJTU-SY bearing dataset as an example. The results show that the ensemble tree models including random forest (RF), extremely randomized trees (ETs), and deep learning tree model (multi-Grained Cascade Forest, i.e. gcForest) are high-precision and strong robust models suiting industrial application of this task, which have better-performing detecting accuracy and stability than conventional machine learning and single tree models (decision tree and extremely randomized tree). gcForest achieves 99.37% test accuracy using only 3% of the training samples, while RF and ETs also exceed 98%, which outperform eXtreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), categorical boosting (CatBoost), and neural tree models, i.e. Neural Network with Random Forest (NNRF) and TabNet. RF and ETs are better suited for real-time industrial detection tasks in terms of time consumption. This study provides a scientific basis for the rational selection of rolling bearing fault diagnosis methods.