Maria Chiara Leva
Asset management modelling approach integrating structural health monitoring data for composite components of wind turbine blades
Leva, Maria Chiara; Patelli, Edoardo; Podofillini, Luca; Wilson, Simon; Wu, Wen; Saleh, Ali; Remenyte-Prescott, Rasa; Prescott, Darren; Ruano, Manuel Chiachio; Chronopoulos, Dimitrios
Authors
Edoardo Patelli
Luca Podofillini
Simon Wilson
WEN WU Wen.Wu@nottingham.ac.uk
Marie Sklodowska-Curie Early Stage Researcher
Ali Saleh
RASA REMENYTE-PRESCOTT R.REMENYTE-PRESCOTT@NOTTINGHAM.AC.UK
Associate Professor
DARREN PRESCOTT Darren.Prescott@nottingham.ac.uk
Assistant Professor
Manuel Chiachio Ruano
Dimitrios Chronopoulos
Abstract
Optimal asset management strategies for wind turbine blades help to reduce their operation and maintenance costs, and ensure their reliability and safety. Structural health monitoring (SHM) can determine the health state of wind turbine blades through implementing damage identification strategies. The main load-bearing structure spar of the wind turbine blade is inside the structure, and hence difficult to inspect. Advanced SHM techniques, such as guided wave monitoring, can be used to monitor the development of cracks in real-time and provide an early indication of their existence. This paper presents a risk-based maintenance model based on the state information provided by SHM. The model is based on Petri nets, and describes the blade degradation and guided wave monitoring processes, inspection and maintenance works. Fatigue test data of composite components is processed to provide input for the model. The reliability of guided wave monitoring is also assessed. The proposed model is able to predict the condition state and expected number of repairs of composite components for wind turbine blades, which can potentially help in making informed asset management decisions during wind turbine blade operation.
Citation
Leva, M. C., Patelli, E., Podofillini, L., Wilson, S., Wu, W., Saleh, A., …Chronopoulos, D. (2022). Asset management modelling approach integrating structural health monitoring data for composite components of wind turbine blades. In Proceedings of the 32nd European Safety and Reliability Conference (ESREL 2022) (1385-1392). https://doi.org/10.3850/978-981-18-5183-4_R22-24-242-cd
Conference Name | 32nd European Safety and Reliability Conference (ESREL 2022) |
---|---|
Conference Location | Dublin, Ireland |
Start Date | Aug 28, 2022 |
End Date | Sep 1, 2022 |
Acceptance Date | Aug 28, 2022 |
Publication Date | Sep 1, 2022 |
Deposit Date | Nov 18, 2022 |
Publicly Available Date | Nov 18, 2022 |
Pages | 1385-1392 |
Book Title | Proceedings of the 32nd European Safety and Reliability Conference (ESREL 2022) |
ISBN | 9789811851834 |
DOI | https://doi.org/10.3850/978-981-18-5183-4_R22-24-242-cd |
Keywords | Asset management; Wind turbine blades; Petri nets; Structural health monitoring; Ultrasonic guided wave monitoring; Reliability of ultrasonic guided wave monitoring |
Public URL | https://nottingham-repository.worktribe.com/output/13753580 |
Publisher URL | https://www.rpsonline.com.sg/proceedings/esrel2022/html/R22-24-242.xml |
Related Public URLs | https://www.esrel2022.com/ |
Files
R22-24-242
(533 Kb)
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