DEGRADATION STAGE RECOGNITION OF BEARING WITHIN LIFE CYCLE

Abstract

Bearings are important components in nuclear plant system, which are widely applied in different types of pump, electromotor and generator. In long term operation status, bearing performance would degenerate due to fatigue and bad lubrication. The damage of bearings would influence the performance and even the safety of devices. Therefore, it is extremely essential to obtain the operational condition information of bearings during the entire life cycle. In order to monitor and identify the bearing conditions in the entire life cycle, a degradation stage recognition method is proposed in this study. The recognition method consists of 4 main steps: firstly, extract a part of samples from the four stages (normal, early fault, medium fault, terminal fault) of bearing life cycle data set; secondly, extract the signal feature with multiscale permutation entropy technique; then, using the dimensional reduced sample features to train a pattern recognition classifier; finally, applying the classifier to identify the signal samples from the life cycle data set. With this automatic recognition method, the performance and degradation stages in the entire life cycle of bearings can be efficiently and effectively identified without additional manual assistance. The feasibility of this method depends on both of the effective recognition technique and the completion of data for classifier training. Thence, the processing technique improvement and the sustained operation data collection are indispensable for extending the method application to other components and devices.