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A Monte Carlo Expectation Maximization Algorithm for Statistical Inference of Weibull Process with Left Censored Data

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, , Citation Jun-Ming Hu and Hong-Zhong Huang 2021 IOP Conf. Ser.: Mater. Sci. Eng. 1043 052017 DOI 10.1088/1757-899X/1043/5/052017

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hujunming@std.uestc.edu

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1 School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, P.R. China

2 School of Transportation and Automotive Engineering, Xihua University, Chengdu, Sichuan 610039, P.R. China

3 Center for System Reliability and Safety, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, P.R. China

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1757-899X/1043/5/052017

Abstract

The Weibull process plays an important role in the failure analysis of repairable systems. In practice, there exists a situation that the data collected are incomplete. Some of the failure data are missing due to various reasons. Statistical inferences of a Weibull process with incomplete data using Monte Carlo expectation maximization algorithm is proposed. The estimation procedures are derived. A case study is performed to illustrate and compare the performances of this algorithm. It is observed that this method is effective and can simplify the estimation.

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10.1088/1757-899X/1043/5/052017