Intelligent Fault Diagnosis of Rolling Bearing based on VMD and Improved Self-training Semi-supervised Ensemble Learning

Abstract

Intelligent fault diagnosis of rolling bearing is of great importance to improve the predictive maintenance ability of key assets in the context of industrial big data and smart manufacturing. Due to the usually high cost or infeasibility of obtaining data labels, large amount of data is unlabeled in practical industrial scenarios, which poses a challenge for conducting data-driven bearing fault diagnosis. In view of the characteristics of non-stationary and low signal-to-noise ratio of bearing vibration signals and the fact of lacking labeled samples but there exist lots of unlabeled samples, this paper proposes an intelligent diagnosis method for bearing faults based on variational mode decomposition (VMD) and improved self-training semi-supervised ensemble learning. Firstly, the original vibration signal is decomposed into several intrinsic mode functions using VMD, then correlation coefficient criterion is used to select the bearing fault feature bands to improve the signal-to-noise ratio, then time domain features are extracted, the labeled samples are expanded by the improved self-training semisupervised learning model, and finally the bearing fault diagnosis model is established based on ensemble learning by stacking method. Through the validation on two different experimental data sets, the proposed method was able to effectively extract the bearing fault feature information and improve the model accuracy by using unlabeled data compared with typical supervised learning models and other comparative models, which can meet the demand for intelligent diagnosis of bearing fault under the scenario of lacking labeled samples in real industries.