Open AccessComparison of wind turbine gearbox vibration analysis algorithms based on feature extraction and classification
Author(s) -
Koukoura Sofia,
Carroll James,
McDonald Alasdair,
Weiss Stephan
Publication year - 2019
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
eISSN - 1752-1424
pISSN - 1752-1416
DOI - 10.1049/iet-rpg.2018.5313
Subject(s) - vibration , turbine , condition monitoring , wind power , feature extraction , computer science , feature (linguistics) , engineering , signal (programming language) , algorithm , pattern recognition (psychology) , artificial intelligence , acoustics , mechanical engineering , linguistics , philosophy , physics , electrical engineering , programming language
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.