A novel MTTF estimator and associated parameter estimation method on heavily censoring data

• Published in: Quality and reliability engineering international Vol. 37; no. 5; pp. 1706 - 1717
• Main Author: Jiang, Renyan
• Format: Journal Article
• Language: English
• Published: Bognor Regis Wiley Subscription Services, Inc 01.07.2021
• Subjects: Estimates; Failure; heavily censored data; Maximum likelihood method; MTTF; Parameter estimation; Reliability analysis; Robustness (mathematics); single‐parameter MLM; statistical characteristics
• ISSN: 0748-8017; 1099-1638
• DOI: 10.1002/qre.2620

Many reliability and maintenance decision problems need to estimate mean‐time‐to‐failure (MTTF) of a particular product component and/or build its life distribution model as early as possible based on field failure data. The field failure data are often heavily censored. In this case, the exponential‐assumption–based method can considerably overestimate the MTTF, and the classical parameter estimation methods such as the maximum likelihood method (MLM) cannot provide robust estimates. This paper aims to address this issue through proposing a novel method to estimate the MTTF and life distribution parameters. The proposed method first derives a nonparametric estimator of MTTF based on the decomposition of the integral expression of the theoretical MTTF and sample statistical characteristics. The estimated MTTF is then combined with a two‐step single‐parameter MLM to estimate the distribution parameters. A numerical experiment is carried out, and a real‐world dataset is analyzed. The results show that the proposed method can provide accurate and robust estimates for the MTTF and distribution parameters. The method is applicable for any life distribution and offers practitioners an efficient tool for reliability analysis.