Development of a prognostics framework for the iron structural material of the s.v. Cutty Sark
Rosunally, Yasmine, Stoyanov, Stoyan, Bailey, Chris, Mason, Peter, Campbell, Sheelagh, Monger, George and Bell, Ian (2009) Development of a prognostics framework for the iron structural material of the s.v. Cutty Sark. In: Proceedings of Sixth International Conference on Condition Monitoring and Machinery Failure Prevention Technologies – CM/MFPT 2009. The British Institute of Non-Destructive Testing / Coxmoor Publishing, Northampton, UK, pp. 674-685.Full text not available from this repository.
The s.v.Cutty Sark is undergoing major conservation to slow down the deterioration of the original Victorian fabric of the ship. In association with this work, a Prognostics Framework is being developed to monitor the “health” of the ship’s iron structures to help ensure a 50 year life once restoration is completed with only minor and acceptable deterioration taking place over time.
This paper outlines the prognostics framework being developed using three prognostics methodologies: Physics-of-Failure (PoF), Precursor Monitoring and Data-Driven methods. These are currently being developed with the aim of integrating them together into the prognostics framework. “Canary” and “Parrot” devices have been designed to mimic the actual mechanisms that would lead to failure of the iron structures. The “canary” devices would fail at a fast rate so act as indicators of problems whereas the “parrot” devices would fail at a rate comparable to that of the iron structure itself. A PoF model based decrease of corrosion rate over time is used to predict remaining life of an iron structure. Mahalanobis Distance (MD) is used as a precursor monitoring technique to obtain a single comparison metric from multiple sensor data to represent anomalies detected in the system which could lead to failures. Data-driven prognostics is carried out using Bayesian Networks to obtain more accurate predictions of remaining life by integrating remaining life data from PoF models with real-time information of possible anomalies in the system using MD analysis results. As a demonstration, PoF models and MD analysis are applied to a pair of “canary” and The Sixth International Conference on Condition Monitoring and Machinery Failure Prevention Technologies 674 “parrot” devices for which corrosion data was generated using temperature and humidity as environmental factors.
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