Fault Detection on Large Slow Bearings

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Jul 5, 2016
Eric Bechhoefer Rune Schlanbusch Tor Inge Waag

Abstract

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 causes 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 was based on vibration, temperature and grease analysis from three wind turbines, where one turbine was suspected of having a faulted main bearing.

How to Cite

Bechhoefer, E., Schlanbusch, R., & Waag, T. I. (2016). Fault Detection on Large Slow Bearings. PHM Society European Conference, 3(1). https://doi.org/10.36001/phme.2016.v3i1.1578
Abstract 421 | PDF Downloads 161

##plugins.themes.bootstrap3.article.details##

Keywords

vibration, bearing diagnostics, temperature, Grease Analysis

References
Bechhoefer, E., & Bernhard, A. (2007). A Generalized Process for Optimal Threshold Setting in HUMS. IEEE Aerospace Conference, Big Sky.
Bechhoefer, E., Duke, A., & Mayhew, E. (2007). A Case for Health Indicators vs. Condition Indicators in Mechanical Diagnostics. American Helicopter Society Forum 63, Virginia Beach.
Bechhoefer, E., He, D., & Dempsey, P. (2011). Gear Threshold Setting Based On a Probability of False Alarm. Annual Conference of the Prognostics and Health Management Society.
Bechhoefer, E., & He, D. (2012). A Process for Data Driven Prognostics, MFPT, Dayton, Ohio.
Bechhoefer, E., & Fang, A., (2012). Algorithms for Embedded PHM. Prognostics and Health Management (PHM), IEEE.
Bechhoefer, E. (2013). Condition Based Maintenance Fault Database for Testing Diagnostics and Prognostic Algorithms. MFPT.
Bechhoefer, E., Van Hecke, B., & He, D. (2013). Processing for Improved Spectral Analysis. PHM Conference.
Byington, C., Safa-Bakhsh, R., Watson, M., & Kalgren, P. (2003). Metrics Evaluation and Tool Development for Health and Usage Monitoring System Technology. HUMS Conference, DSTO-GD-0348.
Darlow, M.S., Badgley, R. H. & Hogg, G. W. (1974). Application of high frequency resonance techniques for bearing diagnostics in helicopter gearboxes. US Army Air Mobility Research and Development Laboratory, Technical Report pp. 74-76.
Dempsy, P. & Keller, J. (2008). Signal Detection Theory Applied to Helicopter Transmissions Diagnostics Thresholds. NASA Technical Memorandum 2008-215262.
Fukunaga, K. (1990). Introduction to Statistical Pattern Recognition. Academic Press, London, p. 75.
Ho, D. & Randall, R. B. (2000). Optimization of bearing diagnostic techniques using simulated and actual bearing fault signals. Mechanical Systems and Signal Processing. 14 (5), 763-788.
Oppenheim, A. V. (1965). Superposition in a class of nonlinear systems (Ph.D. dissertation). Res. Lab. Electronics, Massachusetts Institute of Technology, Cambridge, MA.
Randall, R. (2011). Vibration-based Condition Monitoring: Industrial, Aerospace & Automotive Application. John Wiley, New York.
Section
Technical Papers

Similar Articles

1 2 3 4 5 6 7 8 9 10 > >> 

You may also start an advanced similarity search for this article.