A Novel Health Indicator Based on Cointegration for Rolling Bearings' Run-To-Failure Process.
The extraction of rolling bearings' degradation features has been developed for decades. However, the degradation features always present different trends of different run-to-failure data. To find a consistent indicator of different data will be helpful to establish a general model and explore the nature of bearings' degradation. In this study, we have found there is a trend of similarity between the energy and complexity features. By using the cointegration test, we found the two kinds of features exhibit a certain degree of cointegration relationship. Fused by the cointegration method, we have obtained a novel health indicator which can depict different run-to-failure data in a unified way. The difference between the energy features and complexity features can be explained by the novel health indicator. The indicator has "two-stage" characters. The first stage is the zero-line stage and the second stage is the quickly raise stage, which presents like an exponential function. It is easy to think about using an exponential degradation model to model this indicator. Next, we have compared the indicator to root mean square (RMS) by using the exponential degradation model. It shows that the indicator is more suitable for the exponential degradation model. In this paper, we used eleven run-to-failure data to verify the generality and "two-stage" characters of the proposed indicator. The result shows that the novel indicator is general and effective and that it will promote the development of bearings' prognostics.
['Li|Hongru|H|', 'Li|Yaolong|Y|0000-0002-0099-8363', 'Yu|He|H|']
D000465:Algorithms D019544:Equipment Failure Analysis D014732:Vibration
rolling bearings generalization degradation model vibration signal