Real-time Reliability Evaluation Based on Stochastic Degradation Process and Sequential Joint Distribution Estimation

¹ Anhui Polytechnic University, School of Mechanical and Automotive Engineering, 241000 Wu Hu, China
² Cheng Du Bureau of The Air Force, The Second Military Delegate Room, 550000 Gui Yang, China
³ Air Force Armament Department, Audit Division, 100000 Bei Jing, China
⁴ AVIC Guizhou Liyang Aviation Power Co., Ltd, 550000 Gui Yang, China

^a Corresponding author: gaoqingzhen68@tom.com

Abstract

According to the different performance degradation paths, reliability curves and parameters of each product in service phase, the reliability and remaining life of components at current moment can be evaluated in real-time reliability assessment by integrating real-time status, historical information and service time of components. The realtime reliability evaluation is valued because it is more personalized, precise, real-time and lean than traditional methods. Furthermore, the results from real-time reliability assessment can also represent the health status of component at current time. In the current research, real-time reliability assessment mainly relies on regression analysis and time series analysis, but these two methods are mainly used to describe the component degradation process, and cannot reflect the influence of external random environment on the component state change. At the same time, due to these limitations of time, economy and test conditions, it is also worth studying how to obtain more accurate and practical reliability distribution and determine a detection interval under the condition of less data. Therefore, based on the analysis of real-time reliability evaluation principle, a more appropriate real-time reliability evaluation method and the detection interval decision model are proposed by means of random degradation process, parameter sequence test joint distribution estimation and failure risk. The result from this method is a quantized value, which is convenient for the direct application of the follow-up maintenance decision research. Therefore, the research in this paper has extensive reference value and practical application prospect.