Failure prognostic schemes and database design of a software tool for efficient management of wind turbine maintenance.
Sinha, Yashwant; Steel, John A.
John A. Steel
Wind Turbines require numerous and varied types of maintenance activities throughout their lifespan, the frequency of which increases with years in operation. At present the proportion of maintenance cost to the total cost for wind turbines is significant particularly for offshore wind turbines (OWT) where this ratio is ?35%. If this ratio is to be reduced in-spite of adverse operating conditions, pre-mature component failures and absence of reliability database for wind turbine components, there is a need to design unconventional maintenance scheme preferably by including novel failure prediction methodologies. Several researchers have advocated the use of Artificial Neural Networks (ANN), Bayesian Network Theory (BNT) and other statistical methods to predict failure so as to plan efficient maintenance of wind turbines, however novelty and randomness of failures, nature and number of parameters involved in statistical calculations and absence of required amount of fundamental work required for such advanced analysis have continued to maintain the high cost of maintenance. This work builds upon the benefits of condition monitoring to design methods to predict generic failures in wind turbine components and exhibits how such prediction methods can assist in cutting the maintenance cost of wind turbines. This study proposes using a dedicated tool to assist with failure prediction and planning and execution of wind turbine maintenance. The design and development of such an all-inclusive tool will assist in performing administrative works, inventory control, financial calculations and service management apart from failure prediction in wind turbine components. Its database will contain reference to standard management practices, regulatory provisions, staff details and their skillsets, service call register, troubleshooting manuals, installation guide, service history, details of customers and clients etc. that would cater to multiple avenues of wind turbine maintenance. In order to build such a software package, a robust design of its database is crucial. This work lists prerequisites for choosing a physical database and identifies the benefits of relational database software in controlling large amounts of data of various formats that are stored in such physical databases. Such a database would be an invaluable resource for reliability studies, an area of interest for both academic researchers and the industry that are identifying avenues to economise wind turbine operations.
SINHA, Y. and STEEL, J.A. 2015. Failure prognostic schemes and database design of a software tool for efficient management of wind turbine maintenance. Wind engineering [online], 39(4), pages 453-477. Available from: https://doi.org/10.1260/0309-524X.39.4.453
|Journal Article Type||Article|
|Acceptance Date||Feb 25, 2015|
|Online Publication Date||Aug 1, 2015|
|Publication Date||Aug 1, 2015|
|Deposit Date||Mar 14, 2016|
|Publicly Available Date||Mar 14, 2016|
|Peer Reviewed||Peer Reviewed|
|Keywords||Wind turbines; Failure prediction; Condition based maintenance; Artificial neural network; Bayesian network; Maintenance tool; Database; Offshore wind turbines; Efficient maintenance; Reliability database|
SINHA 2015 Failure prognostic schemes and database
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