Intelligent Diagnostics for Bearing Faults Based on Integrated Interaction of Nonlinear Features

Abstract

An unforeseen fault of the key bearing of production system due to different reasons has the potential to cause an interruption in the entire production line, resulting in economic and production losses. To improve the reliability of industry production, this article presents an intelligent diagnosis method for element rolling bearing based on the integrated interaction relationship of vibration nonlinear features. The nonlinear features of vibration signals are extracted using recurrence quantification analysis (RQA) and regrouped into different subsets of nonredundant features with the same level of discrimination ability through the technology of ReliefF-affinity propagation clustering. The weighted voting variable predictive model class discrimination (WV-VPMCD) is proposed to fully utilize the interaction of RQA features to do intelligent diagnostics for bearing faults. The experimental results have showed that the WV-VPMCD outperformes the conventional intelligent diagnosis methods in terms of accuracy, consistency, stability, and robustness, especially in the case of small number of samples.