Review of Markov Models for Maintenance Optimization in the Context of Offshore Wind

Rafael Dawid, David McMillan, and Matthew Revie
Submission Type: 
Full Paper
AttachmentSizeTimestamp
phmc_15_042.pdf225 KBAugust 24, 2015 - 3:13am

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.

Publication Year: 
2015
Publication Volume: 
6
Publication Control Number: 
042
Submission Keywords: 
wind energy
maintenance optimization
Markov models
POMDP
Deterioration modelling
Hidden Markov Model
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
Model-based methods for fault detection, diagnostics, and prognosis
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