Techniques for Large, Slow Bearing Fault Detection

Eric Bechhoefer, Rune Schlanbusch, and Tor Inge Waag
Publication Target: 
IJPHM
Publication Issue: 
1
Submission Type: 
Full Paper
AttachmentSizeTimestamp
ijphm_16_005.pdf3.81 MBApril 18, 2016 - 5:09am

Large, slow turning bearings remain difficult to analyze for diagnostics and prognostics. For critical equipment, such as drilling equipment top drives, mining equipment, wind turbine main rotors, helicopter swash plates, etc. this poses safety and logistics support problems. An undetected bearing fault can disrupt service, and cause: delays, lost productivity, or accidents. This paper examines a strategy for analysis of large slow bearings to improve the fault detection of condition monitoring systems, thus reducing operations and maintenance cost associated with these bearing faults. This analysis is primarily concerned with vibration, and compared to temperature and grease analysis from three wind turbines, where one turbine was suspected of having a faulted main bearing.

Publication Year: 
2016
Publication Volume: 
7
Publication Control Number: 
005
Page Count: 
11
Submission Keywords: 
Envelope Analysis
temperature
Cyclostationarity
Grease
Bearing Fault
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
Submitted by: 
  
 
 
 

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