Prognostics based on the generalized diffusion process with parameters updated by a sequential Bayesian method
Prognostics based on the generalized diffusion process with parameters updated by a sequential Bayesian method作者机构：School of Missile Engineering Rocket Force University of Engineering School of Mechanical Engineering Xi'an Jiaotong University
出 版 物：《Science China(Information Sciences)》 (中国科学:信息科学(英文版))
年 卷 期：2022年第65卷第6期
页 面：172-188页
核心收录：
学科分类：0711[理学-系统科学] 02[经济学] 0202[经济学-应用经济学] 020208[经济学-统计学] 07[理学] 0806[工学-冶金工程] 0714[理学-统计学（可授理学、经济学学位）] 070103[理学-概率论与数理统计] 0811[工学-控制科学与工程] 0701[理学-数学]
基 金：supported by National Natural Science Foundation of China (Grant Nos. 61833016, 61922089, 61773386, 61573365, 61573366, 61903376, 61773389, 61673311) Shaanxi National Science Foundation (Grant Nos. 2020JQ-489, 2020JM-360, 2020JQ-298) National Key R&D Program of China (Grant No. 2018YFB1306100)
主 题：generalized diffusion process stochastic model parameters remaining useful life maximum likelihood estimation sequential Bayesian methods
摘 要：The realistic degradation process for the engineering equipment is generally stochastic and complicated owing to the uncertain operational condition and multiple functional loading, exhibiting the absolute nonlinear distinction. Such a nonlinear degradation process is widely modeled as a generalized diffusion process. When utilizing the generalized diffusion process-based model, certain model parameters are considered as the random variables to characterize the unit-to-unit discrepancies. Hence, the estimation of these kinds of parameters usually resorts to the Bayesian method. However, owing to the complex pattern of the model parameters in the generalized diffusion process, computing the Bayesian updated parameters requires plenty of repeated calculation and integration operations once the new degradation monitoring information is available. This will inevitably lower the computing efficiency and real-time performance. Toward this end, this paper presents an adaptive prognostic method based on the generalized diffusion process to determine the remaining useful life(RUL) of degraded equipment. First, a generalized diffusion process-based degradation modeling framework is constructed to describe the health performance of stochastic degraded equipment under complex conditions. Then, we utilize the maximum likelihood estimation(MLE) method to estimate the initial model parameters by analyzing the historical degradation information. Furthermore, a sequential Bayesian method is proposed to recursively update the stochastic model parameters in the generalized diffusion process for particular equipment in service. Unlike the existing studies utilizing the Bayesian method,the primary contrast in the presented method lies in that there is no need to implement the calculation process with complicated integration repeatedly utilizing the whole degradation information obtained before the current time. Particularly, the current measured information is incorporated into the esti
同方期刊数据库