Proceedings of the

The 33rd European Safety and Reliability Conference (ESREL 2023)
3 – 8 September 2023, Southampton, UK

An Asset Management Framework for Wind Turbine Blades Considering Reliability of Monitoring System

Wen Wu1, Darren Prescott2,a, Rasa Remenyte-Prescott2,b, Ali Saleh3,c and Manuel Chiachio Ruano3,d

1Institute for Aerospace Technology & Resilience Engineering Research Group, University of Nottingham, UK.

2Resilience Engineering Research Group, University of Nottingham, UK.

3Department of Structural Mechanics and Hydraulic Engineering, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada (UGR), Granada 18001, Spain.


In this study, a wind turbine (WT) blade asset management (AM) Petri net (PN) model is presented, which incorporates risk-based maintenance and structural health monitoring (SHM). Firstly, PN modules cover the entirety of the bladeAMprocess, describing degradation, condition monitoring, and maintenance processes. The PN model is used to predict the future blade condition for a given AM strategy and provide information to support AM decisionmaking for blades during WT operation. Secondly, the monitoring system reliability is considered by calculating expected sensor network information gain/loss using a Bayesian inverse approach. The effect of the monitoring system's accuracy on maintenance cost can be obtained.

Keywords: Asset management, Wind turbine blades, Petri nets, Bayesian inference, Value of Information, Reliability of monitoring system.

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