An asset management framework for wind turbine blades considering reliability of monitoring system

Wu, Wen; Prescott, Darren; Remenyte-Prescott, Rasa; Saleh, Ali; Chiachio Ruano, Manuel

Abstract

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 blade AM process, 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 decision-making 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.

Citation

Wu, W., Prescott, D., Remenyte-Prescott, R., Saleh, A., & Chiachio Ruano, M. (2023, September). An asset management framework for wind turbine blades considering reliability of monitoring system. Presented at 33rd European Safety and Reliability Conference (ESREL 2023), Southampton, UK

• Presentation Conference Type: Other
• Conference Name: 33rd European Safety and Reliability Conference (ESREL 2023)
• Start Date: Sep 3, 2023
• End Date: Sep 7, 2023
• Publication Date: 2023
• Deposit Date: Sep 18, 2023
• Publicly Available Date: Sep 18, 2023
• Series Title: European Conference on Safety and Reliability (ESREL)
• DOI: https://doi.org/10.3850/978-981-18-8071-1_P365-cd
• Keywords: Asset management, Wind turbine blades, Petri nets, Bayesian inference, Value of Information, Reliability of monitoring system
• Public URL: https://nottingham-repository.worktribe.com/output/25361010
• Related Public URLs: https://www.esrel2023.com/
• Additional Information: Extended abstract.