Open AccessRobust wind turbine gearbox fault detection
Author(s) -
Sheldon Jeremy,
Mott Genna,
Lee Hyungdae,
Watson Matthew
Publication year - 2014
Publication title -
wind energy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.743
H-Index - 92
eISSN - 1099-1824
pISSN - 1095-4244
DOI - 10.1002/we.1567
Subject(s) - turbine , set (abstract data type) , engineering , wind power , fault (geology) , renewable energy , data set , fault detection and isolation , computer science , vibration , operations research , artificial intelligence , aerospace engineering , electrical engineering , seismology , geology , actuator , physics , quantum mechanics , programming language
ABSTRACT Impact Technologies' participation in the National Renewable Energy Laboratory's Wind Turbine Gearbox Condition Monitoring Round Robin focused on applying multiple vibration diagnostic algorithms to the provided data set. These approaches have been developed and matured by the team in Department of Defense applications for more than 10 years. Generally, the methods employed by the team worked well, once the challenges and peculiarities of the data set were realized. The results of these automated algorithms were also corroborated with visual spectral analysis. Both the blind results, obtained without knowing details on actual gearbox condition, and the conclusions that were drawn after learning the actual damage are each discussed. The algorithms and results are summarized herein. Finally some conclusions and recommendations are provided that may help guide future tests and analysis efforts. Copyright © 2013 John Wiley & Sons, Ltd.