Techniques for Large, Slow Bearing Fault Detection
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Published
Nov 11, 2020
Eric Bechhoefer
Rune Schlanbusch
Tor Inge Waag
Abstract
Large, slow turning bearings remain difficult to analyze for diagnostics and prognostics. This poses a critical problem for high value assets, such as drilling equipment top drives, mining equipment, wind turbine main rotors, and helicopter swash plates. 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. This helps reduce operations and maintenance cost associated with these bearing faults. This analysis is primarily concerned with vibration, and is compared to temperature and grease analysis. Data was available from three wind turbines, where one of the turbine was suspected of having a faulted main bearing.
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Keywords
Envelope Analysis, temperature, Cyclostationarity, Grease, Bearing Fault
References
Antoni, J., (2007) Cyclic spectral analysis in practice, Mechanical Systems and Signal Processing 21, 597-630.
Antoni, J., (2009) Cyclostationarity by examples, Mechanical Systems and Signal Processing, 23(2009) 987-1036.
Bechhoefer, E. (2013), Condition Based Maintenance Fault Database for Testing Diagnostics and Prognostic Algorithms. Retrieved from http://www.mfpt.org/FaultData/FaultData.htm
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., & Fang, A., (2012) Algorithms for Embedded PHM, Prognostics and Health Management (PHM), IEEE.
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., A Process for Data Driven Prognostics, (2012), MFPT 2012: Dayton, Ohio.
Bechhoefer, E., Van Hecke, B., & He, D., Processing for Improved Spectral Analysis, PHM Conference, 2013.
Boskoski, P., & Juricic, D. (2013), Modeling localized bearing faults using inverse Gaussian mixtures, Annual Conference of the Prognostics and Health Management Society 2013.
Byington, C., Safa-Bakhsh, R., Watson., M., & Kalgren, P. (2003). Metrics Evaluation and Tool Development for Health and Usage Monitoring System Technology. HUMS 2003 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
Djurdjanovic, D., Jay L., & Jun N.,. "Watchdog Agent—an infotronics-based prognostics approach for product performance degradation assessment and prediction." Advanced Engineering Informatics 17.3 (2003): 109-125.
Electrical Power Research Institute, (2010), “Nuclear Maintenance Applications Center: Effective Gear Practices”, Technical Report #1020247.
Fukunaga, K., (1990), Introduction to Statistical Pattern Recognition, Academic Press, London, 1990, page 75.
Ganeriawala, S., (2006) Some Observations of the Detection of Rolling Bearing Outer Race Faults, SpectraQuest, www.spectraquest.com
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.
McFadden, P., (1987) “A revised model for the extraction of periodic waveforms by time domain averaging”, Mechanical Systems and Signal Processing 1 (1) 1987, pages 83-95
Møller, H., et. al., (2014) “Analysis of Grease in Wind Turbine bearing – a tool for condition monitoring. Part 2“, LUBMAT 2014 Proceedings, Manchester, UK.
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, 2011.
Antoni, J., (2009) Cyclostationarity by examples, Mechanical Systems and Signal Processing, 23(2009) 987-1036.
Bechhoefer, E. (2013), Condition Based Maintenance Fault Database for Testing Diagnostics and Prognostic Algorithms. Retrieved from http://www.mfpt.org/FaultData/FaultData.htm
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., & Fang, A., (2012) Algorithms for Embedded PHM, Prognostics and Health Management (PHM), IEEE.
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., A Process for Data Driven Prognostics, (2012), MFPT 2012: Dayton, Ohio.
Bechhoefer, E., Van Hecke, B., & He, D., Processing for Improved Spectral Analysis, PHM Conference, 2013.
Boskoski, P., & Juricic, D. (2013), Modeling localized bearing faults using inverse Gaussian mixtures, Annual Conference of the Prognostics and Health Management Society 2013.
Byington, C., Safa-Bakhsh, R., Watson., M., & Kalgren, P. (2003). Metrics Evaluation and Tool Development for Health and Usage Monitoring System Technology. HUMS 2003 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
Djurdjanovic, D., Jay L., & Jun N.,. "Watchdog Agent—an infotronics-based prognostics approach for product performance degradation assessment and prediction." Advanced Engineering Informatics 17.3 (2003): 109-125.
Electrical Power Research Institute, (2010), “Nuclear Maintenance Applications Center: Effective Gear Practices”, Technical Report #1020247.
Fukunaga, K., (1990), Introduction to Statistical Pattern Recognition, Academic Press, London, 1990, page 75.
Ganeriawala, S., (2006) Some Observations of the Detection of Rolling Bearing Outer Race Faults, SpectraQuest, www.spectraquest.com
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.
McFadden, P., (1987) “A revised model for the extraction of periodic waveforms by time domain averaging”, Mechanical Systems and Signal Processing 1 (1) 1987, pages 83-95
Møller, H., et. al., (2014) “Analysis of Grease in Wind Turbine bearing – a tool for condition monitoring. Part 2“, LUBMAT 2014 Proceedings, Manchester, UK.
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, 2011.
Section
Technical Papers