Prognostic health management techniques for intelligent condition monitoring of offshore renewable generation assets

Herd, David Scott; Flynn, David

doi:10.1049/cp.2013.1732

Cited by 4 publications

(1 citation statement)

References 8 publications

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“…Several studies have been conducted in prognosis field. According to Herd and Flynn (2013), sensors are the main data source for prediction and health management (PHM). Their redundancy and the use of systems knowledge base allow detecting anomalies and ensuring the accuracy of their data.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-component modeling and classification for failure propagation of an offshore wind turbine

Bezzaoucha

Sahnoun²,

Benslimane

2020

IJESM

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Purpose Improving reliability is a key factor in reducing the cost of wind energy, which is strongly influenced by the cost of maintenance operations. In this context, this paper aims to propose a degradation model that describes the phenomenon of fault propagation to apply proactive maintenance that will act on the cause of failure to prevent its reoccurrence as well as to improve future system designs. Design/methodology/approach The methodology adopted consists in identifying the different components of a wind turbine, their causes and failure modes, and then, classifying these components according to their causes of failure. Findings The result is a classification of the different components of a wind turbine according to their failure causes. From the obtained classification, the authors observed that the failure modes for one component are a failure cause for another component, which describes the phenomenon of failure propagation. Originality/value The different classifications existing in the literature depend on the nature, position and function of the different components. The classification of this study consists in grouping the components of a wind turbine according to their failure causes to develop a degradation model considering the propagation of failure in the field of wind turbines.

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Section: Literature Reviewmentioning

confidence: 99%