Picture of neon light reading 'Open'

Discover open research at Strathprints as part of International Open Access Week!

23-29 October 2017 is International Open Access Week. The Strathprints institutional repository is a digital archive of Open Access research outputs, all produced by University of Strathclyde researchers.

Explore recent world leading Open Access research content this Open Access Week from across Strathclyde's many research active faculties: Engineering, Science, Humanities, Arts & Social Sciences and Strathclyde Business School.

Explore all Strathclyde Open Access research outputs...

Review of Markov models for maintenance optimization in the context of offshore wind

Dawid, Rafael and McMillan, David and Revie, Matthew (2015) Review of Markov models for maintenance optimization in the context of offshore wind. In: Proceedings of the Annual Conference of the Prognostics and Health Management Society 2015. PHM Society, pp. 269-279. ISBN 9781936263202

[img]
Preview
Text (Dawid-etal-PHM2015-Markov-models-for-maintenance-optimization-in-the-context-of-offshore)
Dawid_etal_PHM2015_Markov_models_for_maintenance_optimization_in_the_context_of_offshore.pdf - Final Published Version
License: Creative Commons Attribution 3.0 logo

Download (224kB) | Preview

Abstract

The offshore environment poses a number of challenges to wind farm operators. Harsher climatic conditions typically result in lower reliability while challenges in accessibility make maintenance difficult. One of the ways to improve availability is to optimize the Operation and Maintenance (O&M) actions such as scheduled, corrective and proactive maintenance. Many authors have attempted to model or optimize O&M through the use of Markov models. Two examples of Markov models, Hidden Markov Models (HMMs) and Partially Observable Markov Decision Processes (POMDPs) are investigated in this paper. In general, Markov models are a powerful statistical tool, which has been successfully applied for component diagnostics, prognostics and maintenance optimization across a range of industries. This paper discusses the suitability of these models to the offshore wind industry. Existing models which have been created for the wind industry are critically reviewed and discussed. As there is little evidence of widespread application of these models, this paper aims to highlight the key factors required for successful application of Markov models to practical problems. From this, the paper identifies the necessary theoretical and practical gaps that must be resolved in order to gain broad acceptance of Markov models to support O&M decision making in the offshore wind industry.