Piao Chen: Estimation of Field Reliability Based on Aggregate Lifetime Data (9:00 AM)
12 April 2021 09:00 | Add to my calendar
Many large organizations have developed ambitious programs to build reliability databases by collecting field failure data from a large variety of components. To make the database concise, the component lifetime data are recorded in an aggregate way in these databases. The data format is different from traditional lifetime data and the statistical inference is challenging. In this talk, we present a general parametric estimation framework for the aggregate data.
In the first part of the talk, we address the failure-censored aggregate data, where each data point is a summation of a series of collective failures representing the cumulative operating time of one component position from system commencement to the last component replacement. We use two common lifetime distributions, i.e., the gamma distribution and the inverse Gaussian distribution, to model the component lifetime. We develop point and interval estimation procedures for the model parameters and the lifetime quantiles. Through extensive simulations, we show that the proposed interval estimation methods uniformly outperform the other competing methods. In the second part of the talk, we consider the time-censored aggregate data, where only the number of component replacements in a component position during an operation time interval is reported. Because the likelihood function based on the time-censored aggregate data is most likely intractable, an approximate Bayesian computation algorithm that does not require evaluating the likelihood function is proposed. Given that there are several candidate distributions, we further propose a model selection procedure to identify an appropriate model for the time-censored aggregate data.
