Abstract Optimizing gas turbine maintenance can reduce total operating costs but raises the risk of unit failures. A large pipeline fleet requires constant up time, but experiences variation in unit-to-unit reliability and availability. There is an ongoing desire to optimize maintenance budget to maximize fleet availability. This can be done using detailed maintenance records; however, analyzing these in-depth for the entire fleet can be cumbersome. In this study, a Bayesian reliability and availability analysis is used to predict failure risks for both the fleet and individual units using only high level summary information including MTBF, MTTR, and asset service factor. Bayesian statistics enable transformation of these summary metrics into predictive distributions for reliability and availability accounting for seasonal and annual variations. The study considers a fleet of gas turbines and compares individual units to the fleet to identify outliers. This provides the first step in prioritizing maintenance decisions.
Satish et al. (Mon,) studied this question.