Estimation of Weibull distribution parameters for irregular interval group failure data with unknown failure times
作者:
A. B. M. Zohrul Kabir,
期刊:
Journal of Applied Statistics
(Taylor Available online 1998)
卷期:
Volume 25,
issue 2
页码: 207-219
ISSN:0266-4763
年代: 1998
DOI:10.1080/02664769823197
出版商: Taylor & Francis Group
数据来源: Taylor
摘要:
This paper presents three methods for estimating Weibull distribution parameters for the case of irregular interval group failure data with unknown failure times. The methods are based on the concepts of the piecewise linear distribution function (PLDF), an average interval failure rate (AIFR) and sequential updating of the distribution function (SUDF), and use an analytical approach similar to that of Ackoff and Sasieni for regular interval group data. Results from a large number of simulated case problems generated with specified values of Weibull distribution parameters have been presented, which clearly indicate that the SUDF method produces near-perfect parameter estimates for all types of failure pattern. The performances of the PLDF and AIFR methods have been evaluated by goodness-of-fit testing and statistical confidence limits on the shape parameter. It has been found that, while the PLDF method produces acceptable parameter estimates, the AIFR method may fail for low and high shape parameter values that represent the cases of random and wear-out types of failure. A real-life application of the proposed methods is also presented, by analyzing failures of hydrogen make-up compressor valves in a petroleum refinery.
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