Targeting Faulty Bearings for an Ocean Turbine Dynamometer

Nicholas Waters, Pierre-Philippe Beaujean, and David Vendittis
Publication Target: 
IJPHM
Publication Issue: 
Special Issue Wind Turbine PHM
Submission Type: 
Full Paper
Supporting Agencies (optional): 
Florida Atlantic University
AttachmentSizeTimestamp
ijphm_13_021.pdf1.1 MBJuly 18, 2013 - 11:18pm

A real-time, vibrations-based condition monitoring method used to detect, localize, and identify a faulty bearing in an ocean turbine electric motor is presented in this paper. The electric motor is installed in a dynamometer emulating the functions of the actual ocean turbine. High frequency modal analysis and power trending are combined to assess the operational health of the dynamometer’s bearings across an array of accelerometers. Once a defect has been detected, envelope analysis is used to identify the exact bearing containing the defect. After a brief background on bearing fault detection, this paper introduces a simplified mathematical model of the bearing fault, followed with the signal processing approach used to detect, locate, and identify the fault. In the results section, effectiveness of the methods of bearing fault detection presented in this paper is demonstrated through processing data collected, first, from a controlled lathe setup and, second, from the dynamometer. By mounting a bearing containing a defect punched into its inner raceway to a lathe and placing an array of accelerometers along the length of lathe, the bearing fault is clearly detected, localized, and identified as an inner raceway defect. Through retroactively trending the data leading to the near-failure of one of the electric motors in the dynamometer, the authors identified a positive trend in energy levels for a specific frequency band present across the array of accelerometers and identify two bearings as possible sources of the fault.

Publication Year: 
2013
Publication Volume: 
4
Publication Control Number: 
021
Page Count: 
15
Submission Keywords: 
Bearing Faults
Fault Detection; Techniques; Ocean Turbine
rotating machinery
defects
modulation; demodulation
renewable energy
turbine engine
ocean engineering
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
Submitted by: 
  
 
 
 

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