Open AccessDetection of faulty high speed wind turbine bearing using signal intensity estimator technique
Author(s) -
Elforjani Mohamed,
Shanbr Suliman,
Bechhoefer Eric
Publication year - 2018
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.2144
Subject(s) - crest factor , bearing (navigation) , turbine , vibration , rotor (electric) , kurtosis , condition monitoring , wind power , estimator , engineering , noise (video) , rotational speed , signal (programming language) , fault (geology) , computer science , acoustics , artificial intelligence , mechanical engineering , statistics , mathematics , geology , bandwidth (computing) , physics , programming language , telecommunications , electrical engineering , seismology , image (mathematics)
Bearings are typically used in wind turbines to support shafts and gears that increase rotational speed from a low speed rotor to a higher speed electrical generator. For various bearing applications, condition monitoring using vibration measurements has remained a subject of intense study to the present day since several decades. Various signal processing techniques are used to analyse vibration signals and extract features related to defects. Statistical indicators such as Crest Factor (CF) and Kurtosis (KU) were reported as very sensitive indicators when the presence of the defects is pronounced, whilst their values may come down to the level of undamaged components when the damage is well advanced. Further, these indicators were applied to an acquired data from proposed diagnostic models, test rigs, and instrumentations that were specifically used for particular research tests, and thus, it is essential to undertake further investigations and analysis to assess the influence of other factors such as the structural noise and other operating conditions on the real‐world applications. With this in mind, the present work proposes Signal Intensity Estimator (SIE) as a new technique to discriminate individual types of early natural damage in real‐world wind turbine bearings. Comparative results between SIE and conventional indicators such as KU and CF are also presented. It was concluded that SIE has an advantage over the other fault indicators if sufficient data are provided.