LightGBM-Based Fault Diagnosis of Rotating Machinery Under Changing Working Conditions Using Modified Recursive Feature Elimination

Abstract

This article presents an intelligent and accurate framework for fault diagnosis of induction motors using light gradient boosting machine (LightGBM). The proposed framework offers promising generalization ability when the testing data contains new unseen operating conditions unavailable during the training process. After the acquisition of vibration signals and feature extraction in multiple domains, we perform an iterative feature selection (FS) approach by utilizing a modified version of recursive feature elimination (RFE) and the features’ importance scores obtained by LightGBM. To prevent overfitting and subsequent selection bias, an outer resampling loop encompasses the whole process of our RFE-LightGBM algorithm. Moreover, instead of the conventional resampling methods based on K-fold cross-validation (CV) or leave-one-out CV (LOOCV), we use a new scheme called leave-one-loading-out CV (LOLO-CV). Leveraging LOLO-CV, the proposed FS method identifies the optimal feature subset, making the fault diagnosis robust under changing operating conditions. Then, the final classification is performed with optimal feature subset by training a new LightGBM model with adjusted hyperparameters employing Bayesian optimization. Experimental results from two real case studies show that our proposed fault diagnosis framework achieves accuracies between 98.55% and 100% for various testing scenarios. For example, for the worst-case testing scenario in the bearing dataset of Case Western Reserve University where the no-load data (0hp) is absent during the training process and is only used for testing, the testing accuracy of LightGBM classifier before and after applying the proposed RFE-LightGBM-FS method is 88.04% to 97.23%, respectively. Using the Bayesian hyperparameter optimization further improves the accuracy to 98.55%.

Keywords

• Electrical machines
• bearings
• fault diagnosis
• feature importance
• gradient boosting
• hyperparameter optimization