融合加速退化和现场实测退化数据的剩余寿命预测方法

doi:10.7527/S1000-6893.2014.0010

Abstract

Abstract:

To address the situation that prior information is accelerated degradation data, a residual life prediction method based on Bayesian inference with non-conjugate prior distribution is proposed. Without presupposing the parameters' distribution types of a Wiener process, the parameters at accelerated stress levels are transformed into normal use stress level through acceleration factors and then the optimally fitted distribution types are determined using Anderson-Darling method. When transforming the parameter values, the relationships between parameters of the Wiener process and accelerated stress are deduced according to the theory proposed by Zhou Yuanquan. The estimates of hyper-parameters can be obtained by maximum likelihood methods, and posterior means of parameters can be inferred by Markov Chain Monte Carlo using WinBUGS software. The practical value and research significance of this study are demonstrated through a certain missile electrical connector lifetime prediction example. The results show that the proposed method can effectively solve the problem of residual life prediction when prior information is accelerated degradation data.

Key words: residual life, accelerated degradation data, non-conjugate prior distribution, Wiener process, acceleration factor

CLC Number:

WANG Haowei, XU Tingxue, ZHAO Jianzhong. Residual Life Prediction Method Fusing Accelerated Degradation and Field Degradation Data[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2014, 35(12): 3350-3357.

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Residual Life Prediction Method Fusing Accelerated Degradation and Field Degradation Data

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