Intelligent integrated maintenance for wind power generation. Issue 3 (6th May 2015)

• Record Type: Journal Article
• Authors: Pattison, D.; Segovia Garcia, M.; Xie, W.; Quail, F.; Revie, M.; Whitfield, R. I.; Irvine, I.
• Is Part Of: Wind energy. Volume 19:Issue 3(2016)
• Page Start: 547
• Page End: 562
• Publication Date: 2015-05-06
• Subjects: reliability centred maintenance -- intelligent condition monitoring -- reliability and maintenance modelling -- maintenance scheduling; Wind power -- Periodicals; 621.312136
• DOI: 10.1002/we.1850
• Languages: English
• ISSNs: 1095-4244

Abstract

Abstract: A novel architecture and system for the provision of Reliability Centred Maintenance (RCM) for offshore wind power generation is presented. The architecture was developed by conducting a bottom‐up analysis of the data required to support RCM within this specific industry, combined with a top‐down analysis of the required maintenance functionality. The architecture and system consists of three integrated modules for intelligent condition monitoring, reliability and maintenance modelling, and maintenance scheduling that provide a scalable solution for performing dynamic, efficient and cost‐effective preventative maintenance management within this extremely demanding renewable energy generation sector. The system demonstrates for the first time the integration of state‐of‐the‐art advanced mathematical techniques: Random Forests, dynamic Bayesian networks and memetic algorithms in the development of an intelligent autonomous solution. The results from the application of the intelligent integrated system illustrated the automated detection of faults within a wind farm consisting of over 100 turbines, the modelling and updating of the turbines' survivability and creation of a hierarchy of maintenance actions, and the optimizing of the maintenance schedule with a view to maximizing the availability and revenue generation of the turbines. © 2015 The Authors. Wind Energy published by John Wiley & Sons Ltd