Hierarchical spline for time series forecasting: An application to Naval
ship engine failure rate
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; unbalanced 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 98 Republic of Korea Naval ships have been modeled as
hierarchical model, 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, including the amount of prior knowledge
of the engine.