Optimizing Maintenance Policies for a Yaw System Using Reliability-Centered Maintenance and Data-Driven Condition Monitoring
2020 (English)In: IEEE Transactions on Instrumentation and Measurement, ISSN 0018-9456, E-ISSN 1557-9662, Vol. 69, no 9, p. 6241-6249Article in journal (Refereed) Published
Abstract [en]
System downtime and unplanned outages massively affect plant productivity; therefore, the reliability, availability, maintainability, and safety (RAMS) disciplines, together with fault diagnosis and condition monitoring (CM), are mandatory in energy applications. This article focuses on the optimization of a maintenance plan for the yaw system used in an onshore wind turbine (WT). A complete reliability-centered maintenance (RCM) procedure is applied to the system to identify which maintenance action is the optimal solution in terms of cost, safety, and availability. The scope of the research is to propose a new customized decision-making diagram inside the RCM assessment to reduce the subjectivity of the procedure proposed in the standard and save the cost by optimizing maintenance decisions, making the projects more cost-efficient and cost-effective. This article concludes by proposing a new diagnostic method based on a data-driven CM system to efficiently monitor the health and detect damages in the WT by means of measurements of critical parameters of the tested system. This article highlights how a reliability analysis, during the early phase of the design, is a very helpful and powerful means to guide the maintenance decision and the data-driven CM.
Place, publisher, year, edition, pages
IEEE, 2020. Vol. 69, no 9, p. 6241-6249
Keywords [en]
Condition monitoring (CM), maintenance, reliability, availability, maintainability, safety (RAMS), reliability-centered maintenance (RCM), wind turbine (WT), yaw
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-80858DOI: 10.1109/TIM.2020.2968160ISI: 000559518800031Scopus ID: 2-s2.0-85089873478OAI: oai:DiVA.org:ltu-80858DiVA, id: diva2:1469309
Note
Validerad;2020;Nivå 2;2020-09-21 (johcin)
2020-09-212020-09-212020-10-07Bibliographically approved