Bearing Performance Degradation Assessment Using Linear Discriminant Analysis and Coupled HMM

Abstract

Bearing is one of the most important units in rotary machinery, its performance may vary significantly under different working stages. Thus it is critical to choose the most effective features for bearing performance degradation prediction. Linear Discriminant Analysis (LDA) is a useful method in finding few feature's dimensions that best discriminate a set of features extracted from original vibration signals. Another challenge in bearing performance degradation is how to build a model to recognize the different conditions with the data coming from different monitoring channels. In this paper, coupled hidden Markov models (CHMM) is presented to model interacting processes which can overcome the defections of the HMM. Because the input data in CHMM are collected by several sensors, and the interacting information can be fused by coupled modalities, it is more effective than HMM which used only one state chain. The model can be used in estimating the bearing performance degradation states according to several observation data. When becoming degradation pattern recognition, the new observation features should be input into the pre-trained CHMM and calculate the performance index (PI) of the outputs, the changing of PI could be used to describe the different degradation level of the bearings. The results show that PI will decline with the increase of the bearing degradation. Assessment results of the whole life time experimental bearing signals validate the feasibility and effectiveness of this method.