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

By incorporating information about asset condition from a monitoring system, engineers can utilize asset management models to manage maintenance activities on wind turbine blades throughout their lifespan. This can lower operating and maintenance costs and increase the life of the blades. The asset management model relies on the monitoring system as a source of information, however, commonly the reliability of the monitoring system is not considered. This paper presents a wind turbine blade asset management Petri net (PN) model that covers the blade asset management process, including degradation, inspection, condition monitoring (CM), and maintenance processes. The paper proposes two contributions. Firstly, while taking into account detailed industry guidelines, the developed model can forecast the future blade condition for a given asset management strategy. Secondly, it investigates the impact of the reliability of the monitoring system on the asset management modelling results. With the aid of the developed model, the number of repair actions and probability distributions of blade condition discovery time are obtained. In addition, the PN gives an indication of how misreporting (underestimation and overestimation) occurs and the extent of the misreporting. The simulation results illustrate the degree of uncertainty introduced into the monitoring results by the reliability of the monitoring system and, consequently, the extent to which this factor influences the maintenance strategies. The proposed model can be used to support asset management decisions when monitoring system performance degrades.

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Publication Details

Journal
Reliability Engineering & System Safety
Published
2024-08-31
DOI
https://doi.org/10.1016/j.ress.2024.110478
Citations
8
Primary Topic
Machine Fault Diagnosis Techniques
Type
article
Field-Weighted Citation Impact
2.63

Funders

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article

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

Ali Saleh, Rasa Remenyte‐Prescott, Manuel Chiachío, Darren Prescott et al.
8 citations
Reliability Engineering & System Safety
Machine Fault Diagnosis Techniques
2.63
article

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

Ali Saleh, Rasa Remenyte‐Prescott, Manuel Chiachío, Darren Prescott, Wen-Jong Wu
article en
8 citations

Abstract

By incorporating information about asset condition from a monitoring system, engineers can utilize asset management models to manage maintenance activities on wind turbine blades throughout their lifespan. This can lower operating and maintenance costs and increase the life of the blades. The asset management model relies on the monitoring system as a source of information, however, commonly the reliability of the monitoring system is not considered. This paper presents a wind turbine blade asset management Petri net (PN) model that covers the blade asset management process, including degradation, inspection, condition monitoring (CM), and maintenance processes. The paper proposes two contributions. Firstly, while taking into account detailed industry guidelines, the developed model can forecast the future blade condition for a given asset management strategy. Secondly, it investigates the impact of the reliability of the monitoring system on the asset management modelling results. With the aid of the developed model, the number of repair actions and probability distributions of blade condition discovery time are obtained. In addition, the PN gives an indication of how misreporting (underestimation and overestimation) occurs and the extent of the misreporting. The simulation results illustrate the degree of uncertainty introduced into the monitoring results by the reliability of the monitoring system and, consequently, the extent to which this factor influences the maintenance strategies. The proposed model can be used to support asset management decisions when monitoring system performance degrades.

Reliability Engineering & System SafetyVol. 252
University of Nottingham (GB), Universidad de Granada (ES), Instituto Andaluz de Ciencias de la Tierra (ES)
European Commission, Horizon 2020 Framework Programme, Horizon 2020, H2020 Marie Skłodowska-Curie Actions
Affordable and clean energy
Openalex Percentile: Top 10%
Machine Fault Diagnosis Techniques
2.63
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