A Method for Automated Cavitation Detection with Adaptive Thresholds
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Published
Nov 19, 2020
Seth W. Gregg
John P.H. Steele
Douglas L. Van Bossuyt
Abstract
Hydroturbine operators who wish to collect cavitation intensity data to estimate cavitation erosion rates and calculate remaining useful life (RUL) of the turbine runner face several practical challenges related to long term cavitation detection. This paper presents a novel method that addresses these challenges including: a method to create an adaptive cavitation threshold, and automation of the cavitation detection process. These two strategies result in collecting consistent cavitation intensity data. While domain knowledge and manual interpretation are used to choose an appropriate cavitation sensitivity parameter (CSP), the remainder of the process is automated using both supervised and unsupervised learning methods. A case study based on ramp-down data, taken from a production hydroturbine, is presented and validated using independently gathered survey data from the same hydroturbine. Results indicate that this fully automated process for selecting cavitation thresholds and classifying cavitation performs well when compared to manually selected thresholds. This approach provides hydroturbine operators and researchers with a clear and effective way to perform automated, long term, cavitation detection, and assessment.
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Keywords
health monitoring, Hydroturbine, Cavitation, Hydropower, Mahalanobis
References
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Bajic, B., Services, K. C., Gmbh, K., & Zithe, S. (2003). Methods for vibro-acoustic diagnostics of turbine cavitation Méthodes pour le diagnostic vibro-acoustique de la cavitation de turbine. Analysis, 41(1), 87–96.
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Bourdon, P., Farhat, M., Simoneau, R., Pereira, F., Dupont, P., Avellan, F., & Dorey, J. (1996). Cavitation Erosion Prediction on Francis Turbines - Part 1 Measurements on the Prototype. In Hydraulic machinery and cavitation: Proceedings of the xciii iahr symposium on hydraulic machinery and cavitation (pp. 534–543).
Bourdon, P., Simoneau, R., & Lavigne, P. (1989). A vibratory Approach to the Detection of Erosive Cavitation. In In-ternational symposium on cavitation noise and erosion in fluid systems. ASME.
Cencˆıc, T., Hocevar, M., & Sirok, B. (2014). Study of Erosive Cavitation Detection in Pump Mode of PumpStorage Hydropower Plant Prototype. ASME J. Fluids Eng., 136(5), 51301. doi: 10.1115/1.4026476
Cooley, J. W., & Tukey, J. W. (1964). An Algorithm for the Machine Calculation of Complex Fourier Series Author ( s ): James W . Cooley and John W . Tukey Published by : American Mathematical Society Stable URL : http://www.jstor.org/stable/2003354 REFERENCES Linked references are available o. Mathematics of Computation, 19(90), 297–301.
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273–297. doi: 10.1007/ BF00994018
De Maesschalck, R., Jouan-Rimbaud, D., & Massart, D. L. (2000). The Mahalanobis distance. Chemometrics and Intelligent Laboratory Systems, 50(1), 1–18. Retrieved from http://
www.sciencedirect.com/science/article/pii/S0169743999000477$\backslash$nhttp://linkinghub.elsevier.com/retrieve/pii/S0169743999000477 doi: 10.1016/S0169-7439(99)00047-7
Dorey, J.M.; Laperrousaz, E.; Avellan, F.; Dupont, P.; Simoneau, R.; Bourdon, P. (1996). Cavitation Erosion Prediction on Francis Turbines - Part 3 Methodologies of Prediction. In Hydraulic machinery and cavitation: Proceedings of the xciii iahr symposium on hydraulic machinery and cavitation.
Dorji, U., & Ghomashchi, R. (2014). Hydro turbine failure mechanisms: An overview. Engineering Failure Analysis, 44, 136–147. Retrieved from http://dx.doi.org/10.1016/j .engfailanal.2014.04.013 doi: 10.1016/j.engfailanal.2014.04.013
Dular, M., & Petkovšek, M. (2015). On the mechanisms of cavitation erosion Coupling high speed videos to damage patterns. Experimental Thermal and Fluid Science, 68, 359–370. Retrieved from http://linkinghub.elsevier.com/retrieve/pii/S0894177715001508 doi:10.1016/j.expthermflusci.2015.06.001
Dular, M., Stoffel, B., & Širok, B. (2006). Development of a cavitation erosion model. Wear, 261(5-6), 642–655. doi: 10.1016/j.wear.2006.01.020
Egusquiza, E., Valero, C., Valentin, D., Presas, A., & Rodriguez, C. G. (2015). Condition monitoring of pump-turbines. New challenges. Measurement, 67, 151–163. Retrieved from http://linkinghub.elsevier.com/retrieve/pii/S0263224115000226 doi:10.1016/j.measurement.2015.01.004
Escaler, X., & Egusquiza, E. (2003). Vibration Cavitation Detection Using Onboard Measurements. Symposium A Quarterly Journal In Modern Foreign Literatures, 1–7.
Escaler, X., Egusquiza, E., Farhat, M., Avellan, F., & Coussirat, M. (2006). Detection of Cavitation in Hydraulic Turbines. Mechanical Systems and Signal Processing, 983 – 1007.
Escaler, X., Ekanger, J. V., Francke, H. H., Kjeldsen, M., & Nielsen, T. K. (2014). Detection of Draft Tube Surge and Erosive Blade Cavitation in a Full-Scale Francis Turbine. Journal of Fluids Engineering, 137(1), 011103. Retrieved from http://fluidsengineering.asmedigitalcollection.asme.org/ article.aspx?doi=10.1115/1.4027541 doi: 10.1115/1.4027541
Figueiredo, E., Park, G., Farinholt, K. M., Farrar, C. R., & Lee, J.-R. (2012). Use of Time-Series Predictive Models for Piezoelectric Active-Sensing in Structural Health Monitoring Applications. Journal of Vibration and Acoustics, 134(4), 041014. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-84863806522{\&}partnerID=tZOtx3y1 doi: 10.1115/1.4006410
Flageul, R. F. P., a Archer, & C. (2012). Numerical and experimental investigations on cavitation erosion. IOP Conference Series: Earth and Environmental Science, 15(2), 22013. Retrieved from http://stacks.iop.org/1755-1315/15/i=2/a=022013 doi: 10.1088/1755-1315/15/2/022013
Francois, L. (2012). Vibratory detection system of Cavitation Erosion: Historic and Algorithm Validation. In Proceedings of the eighth international symposium on cavitation (pp. 325 –330).
Gordon, J. L. (2001). Hydraulic turbine efficiency. Canadian Journal of Civil Engineering, 28(2), 238–253. doi: 10 .1139/l00-102
Gregg, S. W., Steele, J. P. H., & Bossuyt, D. L. V. (2016). Feature Selection for Monitoring Erosive Cavitation on a Hydroturbine. International Journal of Prognostics and Health Management, In Review.
Hammitt, F. G., & De, M. K. (1979). Cavitation damage prediction. Wear, 52(2), 243–262. doi: 10.1016/0043-1648(79)90066-8
Hartigan, J. A. (1975). Clustering Algorithms.
Hartigan, J. A., & Wong, M. A. (1979). A K-Means Clustering Algorithm. Journal of the Royal Statistical Society. Series C (Applied Statistics), 28(1), 100–108. Retrieved from http://www.jstor.org/stable/234683015
Heng, A., Zhang, S., Tan, A. C., & Mathew, J. (2009). Rotating machinery prognostics: State of the art, challenges and opportunities. Mechanical Systems and Signal Processing, 23(3), 724–739. Retrieved from http://linkinghub.elsevier.com/retrieve/pii/S0888327008001489 doi:10.1016/j.ymssp.2008.06.009
Inacio, M., Lemos, A., & Caminhas, W. (2014). Fault Diagnosis with Evolving Fuzzy Classifier Based on Clustering Algorithm and Drift Detection. In Annual conference of the prognostics and health management society (Vol. 2014).
ISO. (2005). Mechanical Vibration – ISO 7919-5:2005: Evaluation of machine vibration by measurements on rotating shafts – Part 5: Machine sets in hydraulic power generating and pumping plants.
Jardine, A. K., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7), 1483–1510. Retrieved from http://linkinghub.elsevier.com/retrieve/pii/S0888327005001512 doi:10.1016/j.ymssp.2005.09.012
Jian, W., Petkovšek, M., Houlin, L., Širok, B., & Dular, M. (2015). Combined Numerical and Experimental Investigation of the Cavitation Erosion Process. Journal of Fluids Engineering, 137(5), 051302. Retrieved from http://fluidsengineering.asmedigitalcollection.asme.org/article.aspx?doi=10.1115/1.4029533 doi: 10.1115/1.4029533
Kan, M. S., Tan, A. C., & Mathew, J. (2015). A review on prognostic techniques for nonstationary and non-linear rotating systems. Mechanical Systems and Signal Processing, 62-63, 1–20. Retrieved from http://www.sciencedirect.com/science/article/pii/S0888327015000898 doi:10.1016/j.ymssp.2015.02.016
Keogh, E., & Kasetty, S. (2002). On the need for time series data mining benchmarks. Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD ’02, 102. Retrieved from http://dl.acm.org/citation.cfm?id=775047.775062 doi: 10.1145/775047.775062
Khelf, I., Laouar, L., Bouchelaghem, A. M., Rémond, D., & Saad, S. (2013). Adaptive fault diagnosis in rotating machines using indicators selection. Mechanical Systems and Signal Processing, 40(2), 452–468. Retrieved from http://dx.doi.org/10.1016/j.ymssp.2013.05.025 doi: 10.1016/j.ymssp.2013.05.025
Khurana, S., Navtej, & Singh, H. (2012). Effect of cavitation on hydraulic turbines- A review. International Journal of Current Engineering and Technology, 2(1), 172–177. The Knowledge Stream - Detecting Cavitation to Protect and Maintain Hydraulic Turbines. (2014). (Summer 2014). Retrieved from https://www.usbr.gov/research/docs/ updates/2014-14-cavitation.pdf
Kumar, P., & Saini, R. P. (2010). Study of cavitation in hydro turbines-A review. Renewable and Sustainable Energy Reviews, 14(1), 374–383. doi: 10.1016/j.rser.2009.07.024
Lloyd, S. P. (1982). Least Squares Quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129– 137. doi: 10.1109/TIT.1982.1056489
McKee, K. K., Forbes, G. L., Mazhar, I., Entwistle, R., Hodkiewicz, M., & Howard, I. (2015). A vibration cavitation sensitivity parameter based on spectral and statistical methods. Expert Systems with Applications, 42(1), 67–78. Retrieved from http://linkinghub.elsevier.com/retrieve/pii/S0957417414004357 doi:10.1016/j.eswa.2014.07.029
Milligan, G. W., & Cooper, M. C. (1988). A study of standardization of variables in cluster analysis. Journal of Classification, 5(2), 181–204. doi: 10.1007/ BF01897163
Montgomery, D. C., & Runger, G. C. (2007). Applied Statistics and Probablility for Engineers (4th ed.). John Wiley & Sons. Nandi, A. K., Liu, C., & Wong, M. L. D. (n.d.). Intelligent Vibration Signal Processing for Condition Monitoring., 1–15.
Pal, N. R., & Pal, S. K. (1993). A review on image segmentation techniques. Pattern Recognition, 26(9), 1277– 1294. doi: 10.1016/0031-3203(93)90135-J
Pennacchi, P., Borghesani, P., & Chatterton, S. (2015). A cyclostationary multi-domain analysis of fluid instability in Kaplan turbines. Mechanical Systems and Signal Processing, 60-61, 375–390. Retrieved from http://linkinghub.elsevier.com/retrieve/pii/S0888327015000163 doi:10.1016/j.ymssp.2014.08.026
Pollard, D. (1981). Strong Consistency of K-Means Clustering. The Annals of Statistics, 9(1), 135–140. doi: 10.1214/aos/1176345339
Ramasso, E., & Saxena, A. (2014). Performance Benchmarking and Analysis of Prognostic Methods for CMAPSS Datasets. International Journal of Prognostics and Health Management(ISSN2153-2648), 1–15.
Rus, T., Dular, M., Sirok, B., Hocevar, M., & Kern, I. (2007). An Investigation of the Relationship Between Acoustic Emission, Vibration, Noise, and Cavitation Structures on a Kaplan Turbine. Journal of Fluids Engineering, 129(September), 1112.
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