Currently, the wind energy industry is swiftly changing its maintenance strategy from schedule based maintenance to predictive based maintenance. Condition monitoring systems (CMS) play an important role in the predictive maintenance cycle. As condition monitoring systems are being adopted by more and more OEM and O&M service providers from the wind energy industry, it is crucial to effectively interpret the data generated by the CMS and initiate proactive processes to efficiently reduce the risk of potential component or system failure which often leads to down tower repair or gearbox replacement. The majority of CMS are designed and constructed based on vibration analysis which has been refined over the years by researchers and scientists. This paper provides detailed description and mathematical interpretation of a comprehensive selection of condition indicators for gears, bearings and shafts. Since different condition indicators are sensitive to different kind of failure modes, the application for each condition indicators were also discussed. The Time Synchronous Averaging (TSA) algorithm was applied as the signal processing method before the extraction of condition indicators for gears and shafts. Time Synchronous Resampling algorithm was applied to stabilize the shaft speed before the extraction of bearing condition indicators. Several case studies of real world wind turbine component failure detection using condition indicators were presented to demonstrate the effectiveness of certain condition indicators.
How to Cite
Condition Indicator, Condition monitoring system, case study
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