Open AccessA comparative study on vibration‐based condition monitoring algorithms for wind turbine drive trains
Author(s) -
Siegel David,
Zhao Wenyu,
Lapira Edzel,
AbuAli Mohamed,
Lee Jay
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.1585
Subject(s) - bearing (navigation) , turbine , engineering , condition monitoring , wind power , reliability (semiconductor) , pinion , drivetrain , operational modal analysis , vibration , frequency domain , kurtosis , residual , computer science , algorithm , structural engineering , torque , modal analysis , aerospace engineering , artificial intelligence , acoustics , mechanical engineering , mathematics , power (physics) , quantum mechanics , computer vision , thermodynamics , statistics , physics , rack , finite element method , electrical engineering
The ability to detect and diagnose incipient gear and bearing degradation can offer substantial improvements in reliability and availability of the wind turbine asset. Considering the motivation for improved reliability of the wind turbine drive train, numerous research efforts have been conducted using a vast array of vibration‐based algorithms. Despite these efforts, the techniques are often evaluated on smaller‐scale test‐beds, and existing studies do not provide a detailed comparison between the various vibration‐based condition monitoring algorithms. This study evaluates a multitude of methods, including frequency domain and cepstrum analysis, time synchronous averaging narrowband and residual methods, bearing envelope analysis and spectral kurtosis‐based methods. A full‐scale baseline wind turbine drive train and a drive train with several gear and bearing failures are tested at the National Renewable Energy Laboratory (NREL) dynamometer test cell during the NREL Gear Reliability Collaborative Round Robin study. A tabular set of results is presented to highlight the ability of each algorithm to accurately detect the bearing and gear wheel component health. The results highlight that the cepstrum and the narrowband phase modulation signal were effective methods for diagnosing gear tooth problems, whereas bearing envelope analysis could confidently detect most of the bearing‐related failures. Copyright © 2013 John Wiley & Sons, Ltd.