A comparative study on vibration‐based condition monitoring algorithms for wind turbine drive trains

Abstract

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.

Current Signature and Vibration Analyses to Diagnose an In-Service Wind Turbine Drive Train

“…Different vibration analysis methods for WT gearboxes are evaluated and presented in [28]. A comparative study extending these results is given in [29]. Apart from classical frequency domain analysis, other methods used in gear diagnostics are cepstrum analysis [30] and time synchronous averaging [31].…”

Feature Mining and Health Assessment for Gearboxes Using Run-Up/Coast-Down Signals

“…According to the vibration model given in Section 2, both the amplitude modulation and phase modulation can be utilized to assess the health status of gearbox. In practice, however, the phase modulation was found to be more effective for detecting incipient faults of gears, and in general yields better results than amplitude modulation [37]. For this reason, it is selected as the target feature in this work.…”

A study of white etching crack bearing failure detection using electrostatic sensing in wind turbine gearboxes

“…In addition, although sensing techniques such as vibration monitoring have been shown to be effective when detecting surface damage in bearings [12,13] , they cannot detect subsurface cracks. However, acoustic emission sensors have shown promising results when detecting subsurface crack formation in rolling element bearings [14,15] .…”