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Open AccessArticle

Bayesian Based Diagnostic Model for Condition Based Maintenance of Offshore Wind Farms

1
Department of Civil Engineering, Aalborg University, DK-9220 Aalborg, Denmark
2
Vattenfall, Business Area Wind, Analytics & Asset Integrity Management, Hoekenrode 8, 1102 BR Amsterdam, The Netherlands
*
Author to whom correspondence should be addressed.
Energies 2018, 11(2), 300; https://doi.org/10.3390/en11020300
Received: 20 November 2017 / Revised: 24 January 2018 / Accepted: 26 January 2018 / Published: 29 January 2018
(This article belongs to the Section Electrical Power and Energy System)
Operation and maintenance costs are a major contributor to the Levelized Cost of Energy for electricity produced by offshore wind and can be significantly reduced if existing corrective actions are performed as efficiently as possible and if future corrective actions are avoided by performing sufficient preventive actions. This paper presents an applied and generic diagnostic model for fault detection and condition based maintenance of offshore wind components. The diagnostic model is based on two probabilistic matrices; first, a confidence matrix, representing the probability of detection using each fault detection method, and second, a diagnosis matrix, representing the individual outcome of each fault detection method. Once the confidence and diagnosis matrices of a component are defined, the individual diagnoses of each fault detection method are combined into a final verdict on the fault state of that component. Furthermore, this paper introduces a Bayesian updating model based on observations collected by inspections to decrease the uncertainty of initial confidence matrix. The framework and implementation of the presented diagnostic model are further explained within a case study for a wind turbine component based on vibration, temperature, and oil particle fault detection methods. The last part of the paper will have a discussion of the case study results and present conclusions. View Full-Text
Keywords: diagnostic; condition based maintenance; offshore wind; O&M; confidence matrix; diagnosis matrix; Bayesian updating; vibration; temperature; oil particle diagnostic; condition based maintenance; offshore wind; O&M; confidence matrix; diagnosis matrix; Bayesian updating; vibration; temperature; oil particle
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MDPI and ACS Style

Asgarpour, M.; Sørensen, J.D. Bayesian Based Diagnostic Model for Condition Based Maintenance of Offshore Wind Farms. Energies 2018, 11, 300.

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