Failure Analysis for Truncated and Fully Censored Lifetime Data With a Hierarchical Grid Algorithm

In the field of failure analysis and reliability evaluation, truncated and censored lifetime data due to observational constraints and periodic inspection schemes are highly common. The expectation-maximization algorithm is a widely employed approach for the parameter estimations of truncated and censored lifetime data. In this study, a new hierarchical grid algorithm is proposed to estimate the model parameters based on left-truncated and fully-censored data. An improved expectation-maximization algorithm is also adapted under incomplete information. These two methods are compared using Monte Carlo simulations. The confidence intervals corresponding to different quantile methods of the nonparametric bootstrap are compared in terms of coverage probabilities. In a case study, the failure analysis and prediction intervals for individual coupler knuckles of rail wagons are discussed. This simulation study and case study verify the effectiveness of the proposed framework.