A Two-Stage Diagnosis Framework for Wind Turbine Gearbox Condition Monitoring



Published Nov 1, 2020
Prasanna Tamilselvan Pingfeng Wang Shuangwen Sheng Janet M. Twomey


Advances in high-performance sensing technologies enable the development of wind turbine condition monitoring systems to diagnose and predict the system-wide effects of failure events. This paper presents a vibration-based two-stage fault detection framework for failure diagnosis of rotating components in wind turbines. The proposed framework integrates an analytical defect detection method with a graphical verification method to ensure diagnosis efficiency and accuracy. The efficacy of the proposed methodology is demonstrated with a case study using the gearbox condition monitoring Round Robin study dataset provided by the National Renewable Energy Laboratory (NREL). The developed methodology successfully detected five faults out of a total of seven with accurate severity levels and without producing any false alarm in the blind analysis. The case study results indicate that the developed fault detection framework is effective for analyzing gear and bearing faults in wind turbine drivetrain systems based on system vibration characteristics.

Abstract 168 | PDF Downloads 240



Gearbox, Condition monitoring, Diagnosis

Antoni, J. (2002). Differential diagnosis of gear and bearing faults. Journal of Vibration and Acoustics, 124 (2), pp. 165-171.
Byon, E., Perez, E., Ntaimo, L., & Ding, Y. (2010). Simulation of wind farm operations and maintenance using DEVS. Simulation, pp. 1-25.
Ebeling, C. E. (1997). An introduction to reliability and maintainability engineering. Long Grove, IL: Waveland.
Errichello, R., & Muller, J. (2012). Gearbox Reliability Collaborative gearbox 1 failure analysis report. NREL/SR-5000-53062. Golden, CO: National Renewable Energy Laboratory.
Gebraeel, N., Lawley, M., & Liu, R. (2002). Vibration-based condition monitoring of thrust bearings for maintenance management. Intelligent Engineering System Through Artificial Neural Network, 12, pp. 543-551.
Harris, T.A. (2001). Rolling bearing analysis. 4th edition. New York, NY: John Wiley & Sons, pp. 993-1000.
Lebold, M., McClintic, K.., Campbell, R., Byington, C., & Maynard, K. (2000). Review of vibration analysis methods for gearbox diagnostics and prognostics. Proceedings of the 54th Meeting of the Society for Machinery Failure Prevention Technology, May 1-4, Virginia Beach, VA, USA.
Lu, W., & Chu, F. (2010). Condition monitoring and fault diagnostics of wind turbines. Proceedings of Prognostics and Health Management Conference, pp. 1-11.
McFadden, P.D., & Smith, J.D. (1984). Vibration monitoring of rolling element bearings by the high-frequency resonance technique-a review. Tribology International, 17 (1), pp. 3-10.
Nielsen, J., & Sorensen, J. (2010). On risk-based operation and maintenance of offshore wind turbine components. Reliability Engineering and System Safety, 96 (2011), pp. 218-229.
Nilsson, J., & Bertling, L. (2007). Maintenance management of wind power systems using condition monitoring systems - Life cycle cost analysis for two case studies. IEEE Transactions on Energy Conversion, 22(1), pp. 223-229.
Randall, R.B. (2011). Vibration-based condition monitoring. Hoboken, NJ: Wiley.
Randall, R.B., & Antoni, J. (2011). Rolling element bearing diagnostics - A tutorial. Mechanical Systems and Signal Processing, 25 (2), pp. 485-520.
Shi, W., Wang, F., Zhuo, Y., & Liu, Y. (2010). Research on operation condition classification method for vibration monitoring of wind turbine. Proceedings of Power and Energy Engineering Conference APPEEC, pp. 1-6, Asia Pacific. Sheng, S. (2012). Wind turbine gearbox condition monitoring round robin study – Vibration analysis. NREL/TP-5000-54530. Golden, CO: National Renewable Energy Laboratory.
Sheng, S., Link, L., LaCava, W., van Dam, J., McNiff, B., Veers, P., Keller, J., Butterfield, S., & Oyague, F. (2011). Wind turbine drivetrain condition monitoring during GRC phase 1 and phase 2 testing. NREL/TP-5000-52748. Golden, CO: National Renewable Energy Laboratory.
SpectraQuest Tech Note. (2006). Analyzing gearbox degradation using time-frequency signature analysis.
Tamilselvan, P., Wang, P., & Twomey, J. (2012). Quantification of economic and environmental benefits for prognosis informed wind farm operation and maintenance. 62nd Annual IIE Industrial Systems and Engineering Research Conference (ISERC 2012), Orlando, FL, USA.
Tamilselvan, P., Wang, Y., & Wang, P. (2012). Optimization of wind turbines operation and maintenance using failure prognosis. IEEE 2012 Prognostics and Health Management (PHM 2012), Denver, CO, USA.
Tamilselvan, P., Wang, P., & Jayaraman, R. (2012). Diagnostics with unexampled faulty states using a two-fold classification method. 2012 IEEE International Conference on Prognostics and Health Management, June 18-21, Denver, CO, USA.
Tamilselvan, P., & Wang P. (2012). A hybrid inference approach for health diagnostics with unexampled faulty states. AIAA 2012-1784, 53rd AIAA/ASME/ASCE /AHS/ASC Structures, Structural Dynamics, and Materials Conference, April 23-26, Honolulu, Hawaii, USA. Tamilselvan, P., & Wang, P. (2013). Failure diagnosis using deep belief learning based health state classification. Reliability Engineering & System Safety, 115 (2013), pp. 124-135.
Yang, W., Tavner, P., & Wilkinson, M. (2008). Wind turbine condition monitoring and fault diagnosis using both mechanical and electrical signatures. Proceedings of IEEE International Conference on Advanced Intelligent Mechatronics, pp. 1296-1301.
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