TY - CONF A1 - Strack, Bernhard A1 - Frank, Jana A1 - Stich, Volker A1 - Pfau, Florian A2 - Herberger, D. A2 - Hübner, M. T1 - Prescriptive Maintenance for Onshore Wind Turbines T2 - Proceedings of the Conference on Production Systems and Logistics : CPSL 2021 N2 - Electricity generated by wind turbines (WT) is a pillar of the transition to renewable energy [1]. In order to economically utilize WTs, operating and maintenance costs, which account for 25% of total electricity generation costs in onshore WTs, are a focus of cost reduction activities [2]. A prescriptive maintenance approach can support in achieving this goal. Prescriptive maintenance is a maintenance approach, where asset condition data is collected and analyzed to recommend specific actions to prevent breakdowns and reduce downtimes. However, the processing and analysis of data is quite complex. Especially unstructured data (such as comments of service technicians in free text fields) is often left unused, as companies, mostly SMEs lack the capacity to carry out these analyses. In this work we propose an approach to utilize the information from service reports, maintenance reports as well as status records from SCADA systems for the development of a prescriptive maintenance approach to onshore WTs. To achieve this, an ontology was utilized in this approach to codify implicit knowledge of service technicians and aid in making unstructured data usable for further analysis. The ontology was used to link historical service and maintenance reports with status codes, thus enabling automated analysis. In interviews with WT topic experts and through further research, damage mechanisms and corresponding maintenance measures were identified and a measure catalogue was developed to support service and maintenance activities. The recognition of the root cause of problems allows for a prescriptive maintenance approach that recommends targeted actions to reduce downtimes and optimize maintenance activities, it also allows to effectively control the outcome of maintenance activities and optimize their execution. KW - Prescriptive Maintenance KW - Ontology KW - Data Analytics Y1 - 2023 UR - https://epub.fir.de/frontdoor/index/index/docId/2547 UR - https://www.repo.uni-hannover.de/handle/123456789/11369 SP - 489 EP - 498 PB - publish-Ing. CY - Hannover ER -