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Diagnostics and prognostics utilising dynamic Bayesian networks applied to a wind turbine gearbox

Plumley, Charles Edward and Wilson, Graeme and Kenyon, Andrew and Quail, Francis and Zitrou, Athena (2012) Diagnostics and prognostics utilising dynamic Bayesian networks applied to a wind turbine gearbox. In: International Conference on Condition Monitoringand Machine Failure Prevention Technologies, CM & MFPT 2012, 2012-06-12 - 2012-06-14, London.

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    Abstract

    The UK has the largest installed capacity of offshore wind and this is set to increase significantly in future years. The difficulty in conducting maintenance offshore leads to increased operation and maintenance costs compared to onshore but with better condition monitoring and preventative maintenance strategies these costs could be reduced. In this paper an on-line condition monitoring system is created that is capable of diagnosing machine component conditions based on an array of sensor readings. It then informs the operator of actions required. This simplifies the role of the operator and the actions required can be optimised within the program to minimise costs. The program has been applied to a gearbox oil testbed to demonstrate its operational suitability. In addition a method for determining the most cost effective maintenance strategy is examined. This method uses a Dynamic Bayesian Network to simulate the degradation of wind turbine components, effectively acting as a prognostics tool, and calculates the cost of various preventative maintenance strategies compared to purely corrective maintenance actions. These methods are shown to reduce the cost of operating wind turbines in the offshore environment.

    Item type: Conference or Workshop Item (Paper)
    ID code: 41394
    Keywords: diagnostics, prognostics, Bayesian networks, wind turbine, Electrical engineering. Electronics Nuclear engineering
    Subjects: Technology > Electrical engineering. Electronics Nuclear engineering
    Department: Faculty of Engineering > Electronic and Electrical Engineering
    Strathclyde Business School > Management Science
    Related URLs:
    Depositing user: Pure Administrator
    Date Deposited: 11 Oct 2012 15:59
    Last modified: 07 Sep 2014 13:34
    URI: http://strathprints.strath.ac.uk/id/eprint/41394

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