I presented my master's project in October 2023, finishing up my MS in statistics/mathematics at SIUE. The associated paper is titled "Reliability Estimation in Series Systems: Maximum Likelihood Techniques for Right-Censored and Masked Failure Data."
The Problem
In reliability engineering, you often find yourself in an annoying situation: a system fails, but you do not know which component caused the failure. This is called masked failure data. On top of that, some systems are still running when you stop observing them, so you only know they survived at least that long. That is right censoring. Both are common in practice. Identifying the exact failed component is expensive or sometimes impossible.
The project builds a likelihood-based framework that handles both masking and censoring simultaneously, models component lifetimes with Weibull distributions, derives closed-form Fisher information for the exponential special case, and provides bootstrap methods for uncertainty quantification. I implemented it all in an R package so practitioners can actually use it.
Related Work
This connects to several other posts and projects:
- Closed-Form Results for Masked Exponential Series Systems covers the exponential distribution special case with analytical solutions
- likelihood.model R package is the software implementation
See the full project page here.
