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Hierarchical spline for time series forecasting: An application to Naval ship engine failure rate
  • Hyunji Moon,
  • Jinwoo Choi
Hyunji Moon
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Jinwoo Choi
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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.

Peer review status:UNDER REVIEW

31 Aug 2020Submitted to Applied AI Letters
31 Aug 2020Assigned to Editor
31 Aug 2020Submission Checks Completed
02 Sep 2020Reviewer(s) Assigned