Real-Time Reliability Evaluation and Life Prediction for Bearings Based on Normalized Individual State Deviation

Abstract

Most of the existing methods for bearing real-time reliability evaluation employ real-time transformation of traditional reliability indices, performance degradation trajectory analysis, and performance degradation distribution, which are usually limited in terms of accuracy and applicability. A method for real-time reliability evaluation and life prediction for bearings based on normalized individual state deviation is proposed in this study. First, a self-organizing map neural network is utilized to obtain the individual state deviation of a running rolling bearing. Second, individual state deviation is normalized into a state deviation degree, which is used to formulate a modified real-time reliability model for the realization of real-time reliability evaluation and residual life prediction. The proposed method combines population information with real-time monitoring information of individual bearings, and thus avoids the negligence of the real-time transformation of the monitored individual. The errors caused by the randomness of the individual bearing operational process are also reduced. Finally, the feasibility and efficiency of the proposed method is validated by performing run-to-failure experiments on bearings.