NDLI: Predictive diagnosis for offshore wind turbines using holistic condition monitoring

Content Provider	IEEE Xplore Digital Library
Author	Migueláñez, E. Lane, D.
Copyright Year	2010
Description	Author affiliation: SeeByte, Edinburgh, UK (Migueláñez, E.; Lane, D.)
Abstract	This paper presents the role of SeeByte's RECOVERY system within the wind energy industry, with specific focus on an offshore scenario. The current generation of condition monitoring systems (CMS) in relation to wind energy are generally provided by turbine manufacturers, or have been adapted from other industries. These systems have a propensity to focus explicitly on individual parts of the turbine (e.g. gearbox and bearings), therefore they are limited in monitoring scope and do not benefit from a system-wide view allowing an understanding of cause and effect across all parts of the turbine. This poor overview of the turbine system means that systems available today are seen as being prone to false alarms which lead to incorrect diagnosis (e.g. incorrect recognition of a sensor failure or of the cause of a vibration) and unnecessary intervention, the `cry wolf' syndrome, where ultimately important warnings are ignored. In addition, this approach could lead to costly incorrect diagnoses. To fully realise the potential of condition monitoring and its impact on decision making/maintenance scheduling, RECOVERY, as a holistic condition monitoring system, is able to monitor the entire wind turbine in an integrated manner. This proposed holistic system takes a broad view of events and sensor values across the complete turbine system and subsystems (also across a complete farm) to improve diagnostic correctness and reduce no-fault-found situations.
Starting Page	1
Ending Page	7
File Size	962595
Page Count	7
File Format	PDF
ISBN	9781424443321
DOI	10.1109/OCEANS.2010.5664584
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-09-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wind turbines Ontologies Condition monitoring Numerical models Monitoring
Content Type	Text
Resource Type	Article

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