Wind Turbine Bearing Fault Detection Using Adaptive Resampling and Order Tracking
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Abstract
Wind energy is growing increasingly popular in the United States, so it is imperative to make it as cost competitive as possible. Operations and Maintenance (O&M) make up 20-25% of the total cost of onshore wind projects. Unplanned maintenance contributes approximately 75% of the total maintenance costs (WWEA, 2012). Condition-based maintenance strategies intend to maximize the uptime by reducing to the amounts of unplanned maintenance. This should result in an overall decrease in the cost of maintenance. Wind turbines produce an interesting challenge, because their main shaft rotation is both slow and nonstationary. Through the use of adaptive resampling and order tracking, both of these challenges were combated as the bearing fault was identified in the order spectrum then tracked as it progressed. The fault was identified as an outer race defect on the main bearing that initiated sometime during or before installation. The total energy in the order spectrum around the bearing fault rate was identified as a potential front-runner for a prognostic parameter. This paper presents a case study application to operational wind turbine bearing data to demonstrate the ease and intuitiveness of combining adaptive resampling and order tracking to diagnose faults for slow, nonstationary bearings. Prognosis of remaining useful life is proposed with features extracted from the order spectrum, but additional data are needed to develop and demonstrate this analysis.
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fault detection, vibration analysis, Wind Turbines
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