Bayesian based Prognostic Model for Predictive Maintenance of Offshore Wind Farms

Masoud Asgarpour and John Dalsgaard Sørensen
Publication Target: 
IJPHM
Publication Issue: 
1
Submission Type: 
Full Paper
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ijphm_18_010.pdf612.96 KBMarch 14, 2018 - 12:14am

The operation and maintenance costs of offshore wind farms can be significantly reduced if existing corrective actions are performed as efficient as possible and if future corrective actions are avoided by performing sufficient preventive actions. In this paper a prognostic model for degradation monitoring, fault prediction and predictive maintenance of offshore wind components is defined.
The diagnostic model defined in this paper is based on degradation, remaining useful lifetime and hybrid inspection threshold models. The defined degradation model is based on an exponential distribution with stochastic scale factor modelled by a normal distribution. Once based on failures, inspection or condition monitoring data sufficient observations on the degradation level of a component are available, using Bayes’ rule and Normal-Normal model prior exponential parameters of the degradation model can be updated. The components of the diagnostic model defined in this paper are further explained within several illustrative examples. At the end, conclusions are given and recommendations for future studies on this topic are discussed.

Publication Year: 
2018
Publication Volume: 
9
Publication Control Number: 
010
Page Count: 
9
Submission Keywords: 
Prognostic; Predictive Maintenance; Offshore Wind; O&M; Degradation Model; Remaining Useful Lifetime Model; Bayesian Updating
Submission Topic Areas: 
Component-level PHM
Model-based methods for fault detection, diagnostics, and prognosis
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