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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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Abstract

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:ACCEPTED

31 Aug 2020Submitted to Applied AI Letters
31 Aug 2020Submission Checks Completed
31 Aug 2020Assigned to Editor
02 Sep 2020Reviewer(s) Assigned
05 Oct 2020Review(s) Completed, Editorial Evaluation Pending
05 Oct 2020Editorial Decision: Revise Major
11 Nov 20201st Revision Received
11 Nov 2020Assigned to Editor
11 Nov 2020Submission Checks Completed
12 Dec 2020Reviewer(s) Assigned
14 Jan 2021Review(s) Completed, Editorial Evaluation Pending
14 Jan 2021Editorial Decision: Revise Minor
05 Mar 20212nd Revision Received
05 Mar 2021Submission Checks Completed
05 Mar 2021Assigned to Editor
09 Mar 2021Reviewer(s) Assigned
13 Mar 2021Review(s) Completed, Editorial Evaluation Pending
13 Mar 2021Editorial Decision: Accept