Analyzing highly censored reliability data without exact failure times: An efficient tool for practitioners
There are many real-life situations where exact failure times are not available or not easily available for reliability analysis. Motivated by this problem, this work is concerned with the analysis of reliability data without exact failure times. The unavailability of exact failure times has compelled us to develop a simpliﬁed version of the maximum likelihood (ML) estimation for reliability parameters and measures. The approach is applicable to singleparameter reliability models including the exponential reliability model and the Weibull reliability model with an assumed or known shape parameter value. In the latter case, it takes advantage of the fact that in many practical situations a reasonable estimate of the Weibull shape parameter is attainable by certain means. This is particularly valuable for reliability assessment of highly censored Weibull data with only a few failures, because in such situations it is highly desirable or necessary to exploit prior knowledge of the Weibull shape parameter to compensate for the limited information contained in the data in the hope of making a sound assessment. This is an application in a sense of the ideas and principles of the emerging paradigm of statistical engineering. The methods are developed by practitioners and for practitioners. We show that they offer practitioners a handy and efﬁcient tool for reliability analysis, using hard disk drive product testing as examples.