Comparison of wind turbine gearbox vibration analysis algorithms based on feature extraction and classification. Issue 14 (15th August 2019)

• Record Type: Journal Article
• Authors: Koukoura, Sofia; Carroll, James; McDonald, Alasdair; Weiss, Stephan
• Is Part Of: IET renewable power generation. Volume 13:Issue 14(2019)
• Page Start: 2549
• Page End: 2557
• Publication Date: 2019-08-15
• Subjects: wind power plants -- maintenance engineering -- fault diagnosis -- mechanical engineering computing -- pattern recognition -- wind turbines -- vibrations -- gears -- condition monitoring -- feature extraction; diagnostic algorithms -- classification accuracy -- wind turbine gearbox vibration analysis algorithms -- feature extraction -- health state assessment -- wind turbine components -- vital aspect -- wind farm operations -- maintenance costs -- costly components -- vibration condition monitoring -- popular existing gear -- diagnostic methods -- vibration signals -- pattern recognition algorithms -- historic vibration data -- operational wind turbines -- healthy operating condition; Renewable energy sources -- Periodicals; 333.79405
• DOI: 10.1049/iet-rpg.2018.5313
• Languages: English
• ISSNs: 1752-1416

Abstract

Abstract : Health state assessment of wind turbine components has become a vital aspect of wind farm operations in order to reduce maintenance costs. The gearbox is one of the most costly components to replace and it is usually monitored through vibration condition monitoring. This study aims to present a review of the most popular existing gear vibration diagnostic methods. Features are extracted from the vibration signals based on each method and are used as input in pattern recognition algorithms. Classification of each signal is achieved based on its health state. This is demonstrated in a case study using historic vibration data acquired from operational wind turbines. The data collection starts from a healthy operating condition and leads towards a gear failure. The results of various diagnostic algorithms are compared based on their classification accuracy.