Long-term potential performance degradation analysis method based on dynamical probability model
With the increment of lifetimes of functional components in complex system, it is difficult or costly to monitor the degradation process for these components directly. In this paper, we propose a novel method to indirectly track the degradation process. The continuous failure time data of associated components is considered as a time series with implications of potential performance degradation information of the long-lifetime functional components (LLFC), which are connected with these associated components. Firstly, in order to compute the mean time-to-failure (MTTF), we present a dynamic probability model based on nonparametric kernel estimator. In this model, the sliding time-window technique is used to extract statistical samples, and conditional probability function of failure time is acquired by kernel estimation, subsequently the MTTF is calculated. Secondly, we apply the elementary renewal theorem to present a mapping model between the cumulative degradation degree of LLFC and the MTTF of the associated component are described. Thirdly, in order to quantitatively evaluate the degradation level of LLFC, we introduce two indicators, probability entropy and relative degradation rate. We apply our method to evaluate the performance degradation of the crystallizer vibro-bench. Results show that the method is available to evaluate the degrees of performance degradation of LLFC even though the degradation data cannot be obtained directly. Meanwhile, the method can auto-adjust itself to meet the different statistical sample to overcome the limitation of parameter distribution model. Furthermore, the method can help us to identify the main reason of breakdown and make correct maintenance decision.
potential performance degradation dynamical probability model associated component long-lifetime functional component