Wind Turbine Bearing Fault Detection Using Adaptive Resampling and Order Tracking

Cody M. Walker and Jamie B. Coble
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Submission Type: 
Full Paper
Supporting Agencies (optional): 
DOE-NE NEUP Fellowship
ijphm_18_027.pdf1.35 MBSeptember 30, 2018 - 5:44am

Wind energy is growing increasingly popular in the United States. Condition-based maintenance strategies can be used to maximize their uptime. Wind turbines produce an inter- esting challenge, because their main shaft is both slow and non-stationary. 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. The fault was identified to be an outer race defect on the main bearing that was initiated sometime during or before installation. The total energy in the order spectrum around the bearing fault rate was determined as a potential front-runner for a prognostic parameter.

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Submission Keywords: 
vibration analysis
Wind Turbines
fault detection
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
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