Skip navigation

Development of a prognostics framework for the iron structural material of the s.v. Cutty Sark

Development of a prognostics framework for the iron structural material of the s.v. Cutty Sark

Rosunally, Yasmine, Stoyanov, Stoyan ORCID: 0000-0001-6091-1226, 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.

Abstract

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.

Item Type: Conference Proceedings
Title of Proceedings: Proceedings of Sixth International Conference on Condition Monitoring and Machinery Failure Prevention Technologies – CM/MFPT 2009
Additional Information: [1] The Sixth International Conference on Condition Monitoring and Machinery Failure Prevention Technologies – CM/MFPT 2009, 23-25 June 2009, Dublin, Ireland. [2] Proceedings available on CD-ROM.
Uncontrolled Keywords: Cutty Sark, prognostics framework, data-driven prognostics, computer modelling, physics-of-failure, PoF
Subjects: Q Science > QA Mathematics
V Naval Science > VM Naval architecture. Shipbuilding. Marine engineering
Pre-2014 Departments: School of Computing & Mathematical Sciences
School of Computing & Mathematical Sciences > Department of Computer Science
School of Computing & Mathematical Sciences > Department of Computer Systems Technology
School of Computing & Mathematical Sciences > Department of Mathematical Sciences
School of Computing & Mathematical Sciences > Centre for Numerical Modelling & Process Analysis > Computational Mechanics & Reliability Group
Related URLs:
Last Modified: 14 Oct 2016 09:11
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
URI: http://gala.gre.ac.uk/id/eprint/4508

Actions (login required)

View Item View Item