  Predicting equipment failure is important because it could improve
availability and cut down the operating budget. Previous literature has
attempted to model failure rate with bathtub-formed function, Weibull
distribution, Bayesian network, or AHP. But these models perform well with a
sufficient amount of data and could not incorporate the two salient
characteristics; imbalanced category and sharing structure. Hierarchical model
has the advantage of partial pooling. The proposed model is based on Bayesian
hierarchical B-spline. Time series of the failure rate of 99 Republic of Korea
Naval ships are modeled hierarchically, where each layer corresponds to ship
engine, engine type, and engine archetype. As a result of the analysis, the
suggested model predicted the failure rate of an entire lifetime accurately in
multiple situational conditions, such as prior knowledge of the engine.
