Ontologie für die Instandhaltung von Onshore Windenergieanlagen

Strack, Bernhard; Lenart, Martin; Frank, Jana; Kramer, Nina

doi:10.1007/s10010-021-00466-x

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…Onshore WT maintenance Wind turbine maintenance task ontology for onshore wind turbines by Strack et al (2021).…”

Section: Wpp Spatial Databasementioning

confidence: 99%

Knowledge engineering for wind energy

Marykovskiy,

Clark,

Day

et al. 2024

Wind Energ. Sci.

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Abstract. With the rapid evolution of the wind energy sector, there is an ever-increasing need to create value from the vast amounts of data made available both from within the domain and from other sectors. This article addresses the challenges faced by wind energy domain experts in converting data into domain knowledge, connecting and integrating them with other sources of knowledge, and making them available for use in next-generation artificial intelligence systems. To this end, this article highlights the role that knowledge engineering can play in the digital transformation of the wind energy sector. It presents the main concepts underpinning knowledge-based systems and summarises previous work in the areas of knowledge engineering and knowledge representation in a manner that is relevant and accessible to wind energy domain experts. A systematic analysis of the current state of the art on knowledge engineering in the wind energy domain is performed with available tools put into perspective by establishing the main domain actors and their needs, as well as identifying key problematic areas. Finally, recommendations for further development and improvement are provided.

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“…Onshore WT maintenance Wind turbine maintenance task ontology for onshore wind turbines by Strack et al (2021).…”

Section: Wpp Spatial Databasementioning

confidence: 99%

Knowledge engineering for wind energy

Marykovskiy,

Clark,

Day

et al. 2024

Wind Energ. Sci.

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“…Nevertheless, Faulstich and Hahn already have examined the added value of an evaluation of event data (automatically generated status messages of WT's and maintenance data from service reports) [17]. Therefore, ReStroK has set the goal to increase the amount of available data, which is made possible through the developed ontology [10].…”

Section: Prescriptive Maintenancementioning

confidence: 99%

“…So far, the research project has developed an ontology that allows to convert the information contained in free text fields into a usable form [10]. Using the ontology, existing maintenance and service reports are automatically evaluated for issues and potential future breakdowns.…”

Section: Introductionmentioning

confidence: 99%

Sociotechnical Implementation of Prescriptive Maintenance for Onshore Wind Turbines

Strack¹,

Frank²,

Stich³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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Electricity generated by wind turbines (WT) is a mainstay of the transition to renewable energy. In order to economically utilize WT is, operating and maintenance costs, which account for 25% of total electricity generation costs in onshore WT's, are a focus of cost reduction activities. Implementing a data-driven prescriptive maintenance approach is one way to achieve this. So far, various approaches for prescriptive maintenance for onshore WT's have been suggested. However, little research has addressed the practical implementation considering sociotechnical aspects. The aim of this paper is therefore to identify success factors for the successful implementation of such a maintenance strategy with clear and holistic guidance on how existing knowledge on prescriptive maintenance from science can be transferred to business practice. These recommendations are developed through case study research and classified in the four structural areas of Acatech's Industry 4.

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“…Apesar da ampla adoção de turbinas eólicas para geração de energia, uma grande preocupação é com relação a manutenção de seus equipamentos, visto que a operação e manutenção representam em média 25% do custos totais de uma turbina eólica [18]. Em caso de falhas, a turbina pode ficar inativa por longos períodos, gerando um prejuízo pela interrupção de energia, além dos altos custos de reparos dada a complexidade de seus componentes [3].…”

Section: Introductionunclassified

Estudo sobre Modelos de Aprendizado de Máquina para Detecção de Falhas em Turbinas Eólicas

Pinna,

Hamacher,

Sá

et al. 2023

Proceeding Series of the Brazilian Society of Computational and Applied Mathematics

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Resumo. A crescente busca por soluções energéticas renováveis tem trazido destaque para soluções como turbinas eólicas, que são as principais responsáveis pela transformação de energia eólica em elétrica. Assim, o monitoramento, diagnóstico e prognóstico de falhas destas turbinas é fundamental para garantir a produção energética de forma contínua. Estas turbinas são monitorados por sensores e os dados oriundos deste monitoramento podem ser utilizados para criar modelos capazes de detectar estágios iniciais de degradação dos componentes que formam as turbinas, garantindo assim que falhas sejam identificadas rapidamente, reduzindo custos em manutenção. No presente trabalho é apresentada uma breve revisão sobre o assunto, além da aplicação de duas técnicas de aprendizado de máquina em uma base de dados real.

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Ontologie für die Instandhaltung von Onshore Windenergieanlagen

Cited by 6 publications

References 12 publications

Knowledge engineering for wind energy

Knowledge engineering for wind energy

Sociotechnical Implementation of Prescriptive Maintenance for Onshore Wind Turbines

Estudo sobre Modelos de Aprendizado de Máquina para Detecção de Falhas em Turbinas Eólicas

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