An Integrated Framework of Drivetrain Degradation Assessment and Fault Localization for Offshore Wind Turbines
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Published
Nov 1, 2020
Wenyu Zhao
David Siegel
Jay Lee
Liying Su
Abstract
As wind energy proliferates in onshore and offshore applications, it has become significantly important to predict wind turbine downtime and maintain operation uptime to ensure maximal yield. Two types of data systems have been widely adopted for monitoring turbine health condition: supervisory control and data acquisition (SCADA) and condition monitoring system (CMS). Provided that research and development have focused on advancing analytical techniques based on these systems independently, an intelligent model that associates information from both systems is necessary and beneficial. In this paper, a systematic framework is designed to integrate CMS and SCADA data and assess drivetrain degradation over its
lifecycle. Information reference and advanced feature extraction techniques are employed to procure heterogeneous health indicators. A pattern recognition algorithm is used to model baseline behavior and measure deviation of current behavior, where a Self-organizing Map (SOM) and minimum quantization error (MQE) method is selected to achieve degradation assessment. Eventually, the computation and ranking of component contribution to the detected degradation offers component-level fault localization. When validated and automated by various applications, the approach is able to incorporate diverse data resources and output actionable information to advise predictive maintenance with precise fault information. The
approach is validated on a 3 MW offshore turbine, where an incipient fault is detected well before existing system shuts down the unit. A radar chart is used to illustrate the fault localization result.
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Keywords
wind energy, SCADA, Drivetrain Degradation, Fault Localization, CMS
References
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Lapira, E., Siegel, D., Zhao, W., Brisset, D., Su, J., Wang, C., AbuAli, M., & Lee, J. (2011). A systematic framework for wind turbine health assessment under dynamic operating conditions. Proceedings of the 24th International Congress on Condition Monitoring and Diagnostics Engineering Management, (1-9), May 30 – June 1, Stavanger, Norway.
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Musial, W., & Ram, B. (2010). Large-scale offshore wind power in the United States: Assessment of opportunities and barriers. National Renewable Energy Laboratory Report No. TP-500-40745.
Peng, Z. K., & Chu, F. L. (2004) Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography. Mechanical Systems and Signal Processing, 18 (2), pp. 199-221. doi:10.1016/S0888-3270(03)00075-X
Peng, Z. K., Tse, P. W., & Chu, F. L. (2005). An improved Hilbert-Huang transform and its application in vibration signal analysis. Journal of Sound and Vibration, 286 (1-2), pp. 187-205. doi: 10.1016/j.jsv.2004.10.005
Qiu, Y., Feng, Y., Tavner, P., Richardson, P., Erdos, G., & Chen, B. (2012). Wind turbine SCADA alarm analysis for improving reliability. Wind Energy, 15 (8), pp. 951- 966. doi:10.1002/we.513
Rafiee, J., Tse, P. W., Harifi, A., & Sadeghi, M. H. (2009). A novel technique for selecting mother wavelet function using an intelligent fault diagnosis system. Expert Systems with Applications, 36 (3), pp. 4862- 4875. doi:10.1016/j.eswa.2008.05.052
Sheng, S., & Veers, P. (2011). Wind turbine drivetrain condition monitoring – An overview. Machinery Failure Prevention Technology: The Applied Systems Health Management Conference, May 10-12, Virginia Beach, VA.
Siegel, D., Zhao, W., Lapira, E., AbuAli, M., & Lee, J. (2013). A comparative study on vibration-based condition monitoring algorithms for wind turbine drive trains. Wind Energy, doi:10.1002/we.1585
Yang, W., Tavner, P. J., & Wilkinson, M. R. (2008). Condition monitoring and fault diagnosis of a wind turbine synchronous generator drive train. IET Renewable Power Generation, 3 (1), pp. 1-11. doi: 10.1049/iet-rpg:20080006
Yu, J., & Wang, S. (2009). Using minimum quantization error chart for the monitoring of process states in multivariate manufacturing processes. Computers & Industrial Engineering, 57 (4), pp. 1300-1312. doi:10.1016/j.cie.2009.06.009
Zhang, X., Wen, G., & Wu, T. (2012). A new time synchronous average method for variable speed operating condition gearbox. Journal of Vibroengineering, 14 (4), pp. 1766-1774.
Antoni, J. (2006). The spectral kurtosis: a useful tool for characterising non-stationary signals. Mechanical Sysetms and Signal Processing, 20 (2), pp. 282-307. doi:10.1016/j.ymssp.2004.09.001
Barszcz, T. & Randall, R. (2009). Application of spectral kurtosis for detection of a tooth crack in the planetary gear of a wind turbine. Mechanical Systems and Signal Processing, 23 (4), pp. 1352-1365. doi:10.1015/j.ymssp.2008.07.019
Bechhoefer, E., He, D., & Dempsey, P. (2011). Gear health threshold setting based on a probability of false alarm. Annual Conference of the Prognostics and Health Management Society, September 25 – 20, Montreal, Canada.
Crabtree, C. J., Feng, Y., & Tavner, P. J. (2010). Detecting incipient wind turbine gearbox failure: A signal analysis method for on-line condition monitoring. Scientific Track Proceedings of European Wind Energy Conference, April 20-23, Warsaw, Poland.
Edwin, W., Theo, V., Henk, B., Luc, R., Xiang, J., & Simon, W. (2008). Assessment of condition monitoring techniques for offshore wind farms. Journal of Solar Energy Engineering, 130 (3), pp. 0310041-0310049.
Entezami, M. (2010). Wind turbine condition monitoring system. Doctoral dissertation report. University of Birmingham, Birmingham, United Kindom. http://www.sampaolesiimpiantielettrici.com/documenti/eolico/Wind_Turbines_Condition_Monitoring_Systems_University_of_Birmingham_2010.pdf
Feng, Y., Qiu, Y., Crabtree, C. J., Long, H., & Tavner, P. J. (2011). Use of SCADA and CMS signals for failure detection and diagnosis of a wind turbine gearbox. European Wind Energy Conference & Exhibition, March 14-17, Brussels, Belgium.
Fraunhofer-Institute for Wind Energy and Energy System Technology. (2005). Final report: Advanced maintenance and repair for offshore wind farms using fault prediction and condition monitoring techniques. http://ec.europa.eu/energy/renewables/wind_energy/doc/offshore.pdf
Global Wind Energy Council. (2012). Global Wind Energy Outlook 2012. http://www.gwec.net/wpcontent/uploads/2012/11/GWEO_2012_lowRes.pdf
Grubbs, F. E. (1969). Procedures for detecting outlying observations in samples. Technometrics. 11 (1), pp. 1-21. doi:10.1080/00401706.1969.10490657
Hameed, Z., Hong, Y. S., Cho, Y. M., Ahn, S. H., & Song, C. K. (2009). Condition monitoring and fault detection of wind turbines and related algorithms: a review. Renewable and Sustainable Energy Reviews, 13 (1), pp. 1-39. doi:10.1016/j.rser.2007.05.008
Jiang, Y., Tang, B., Qin, Y., & Liu, W. (2011). Feature extraction method of wind turbine based on adaptive Morlet wavelet and SVD. Renewable Energy, 36 (8), pp. 2146-2153. doi:10.1016/j.renene.2011.01.009
Kohonen, T. (1990). The self-organizing map. Proceedings of the IEEE, 78 (9), pp. 1464-1480.
Lapira, E., Siegel, D., Zhao, W., Brisset, D., Su, J., Wang, C., AbuAli, M., & Lee, J. (2011). A systematic framework for wind turbine health assessment under dynamic operating conditions. Proceedings of the 24th International Congress on Condition Monitoring and Diagnostics Engineering Management, (1-9), May 30 – June 1, Stavanger, Norway.
LeBlanc, M., & Graves, A. (2011). Condition monitoring systems: trends and cost benefits. NREL Wind Turbine Condition Monitoring Workshop, September 19-20, Broomfield, CO. http://www.nrel.gov/wind/pdfs/day1_sessioni_04_garradhassan_leblanc.pdf
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 (623-634), May 1-4, Virginia Beach, VA, USA
Liu, B., Riemenschneider, S., & Xu, Y. (2006). Gearbox fault diagnosis using empirical mode decomposition and Hilbert spectrum. Mechanical Systems and Signal Processing, 20 (3), pp. 718-734. doi:10.1016/j.ymssp.2005.02.003
Lu, B., Li, Y., Wu, X., & Yang, Z. (2009). A review of recent advances in wind turbine condition monitoring and fault diagnosis. Proceedings of IEEE Power Electronics and Machines in Wind Applications, (1-7), June 24-26, Lincoln, NE.
McMillan, D., & Ault, G. W. (2007). Quantification of condition monitoring benefit for offshore wind turbines. Wind Engineering, 31 (4), pp. 267-285. doi: 10.1260/030952407783123060
Meadows, B. (2011). Offshore wind O&M challenges. NREL Wind Turbine Condition Monitoring Workshop, September 19-20, Broomfield, CO. http://www.nrel.gov/wind/pdfs/day1_sessioni_05_nrel_meadows.pdf
Musial, W., & Ram, B. (2010). Large-scale offshore wind power in the United States: Assessment of opportunities and barriers. National Renewable Energy Laboratory Report No. TP-500-40745.
Peng, Z. K., & Chu, F. L. (2004) Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography. Mechanical Systems and Signal Processing, 18 (2), pp. 199-221. doi:10.1016/S0888-3270(03)00075-X
Peng, Z. K., Tse, P. W., & Chu, F. L. (2005). An improved Hilbert-Huang transform and its application in vibration signal analysis. Journal of Sound and Vibration, 286 (1-2), pp. 187-205. doi: 10.1016/j.jsv.2004.10.005
Qiu, Y., Feng, Y., Tavner, P., Richardson, P., Erdos, G., & Chen, B. (2012). Wind turbine SCADA alarm analysis for improving reliability. Wind Energy, 15 (8), pp. 951- 966. doi:10.1002/we.513
Rafiee, J., Tse, P. W., Harifi, A., & Sadeghi, M. H. (2009). A novel technique for selecting mother wavelet function using an intelligent fault diagnosis system. Expert Systems with Applications, 36 (3), pp. 4862- 4875. doi:10.1016/j.eswa.2008.05.052
Sheng, S., & Veers, P. (2011). Wind turbine drivetrain condition monitoring – An overview. Machinery Failure Prevention Technology: The Applied Systems Health Management Conference, May 10-12, Virginia Beach, VA.
Siegel, D., Zhao, W., Lapira, E., AbuAli, M., & Lee, J. (2013). A comparative study on vibration-based condition monitoring algorithms for wind turbine drive trains. Wind Energy, doi:10.1002/we.1585
Yang, W., Tavner, P. J., & Wilkinson, M. R. (2008). Condition monitoring and fault diagnosis of a wind turbine synchronous generator drive train. IET Renewable Power Generation, 3 (1), pp. 1-11. doi: 10.1049/iet-rpg:20080006
Yu, J., & Wang, S. (2009). Using minimum quantization error chart for the monitoring of process states in multivariate manufacturing processes. Computers & Industrial Engineering, 57 (4), pp. 1300-1312. doi:10.1016/j.cie.2009.06.009
Zhang, X., Wen, G., & Wu, T. (2012). A new time synchronous average method for variable speed operating condition gearbox. Journal of Vibroengineering, 14 (4), pp. 1766-1774.
Section
Technical Papers