Pump Health Monitoring Test Environment for Diagnosing the Erosive Effects from Cavitation
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Published
May 19, 2025
Tedja Verhulst
David Judt
Craig Lawson
Osama Al-Tayawe Geoff Ward Yongmann Chung
https://orcid.org/0000-0001-7860-9797
Osama Al-Tayawe Geoff Ward Yongmann Chung
Abstract
Cavitation occurs frequently in pumps. The subsequent erosion that is caused by cavitation can significantly reduce the operational efficiency and Remaining Useful Life (RUL) of the pump. This study describes a new hybrid Health Monitoring (HM) test environment, used to diagnose permanent damage caused by cavitation erosion in a two-stage centrifugal pump. Flowrate, pressure and motor current measurements are made and compared to Computational Fluid Dynamic (CFD) results. The hybrid-based HM carried out using the three methods provide the facility to develop diagnostics for cavitation erosion damage. The suggested methods will not only aid in HM development, but also select the best operating conditions to carry it out. The Gray Level Method (GLM) is implemented using CFD to predict the erosion areas in the centrifugal pump. A Simscape model is devised to enable development of health monitoring algorithms. Few works have attempted to detect for erosion caused by cavitation. It was found that a high-level agreement was achieved between the Simscape, CFD and test-rig results, with an average error of 0.8%, 2.5%, and 2.0% for current, pressure and flow measurements respectively. The results from this research show the feasibility of developing HM algorithms to detect cavitation erosion in aircraft fuel pumps by fusing model and data-based methods. This is an enabler for a move from time-based to condition-based maintenance, thus reducing aircraft operating costs.
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Keywords
cavitation, diagnostics, pump, health monitoring
References
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Addie, G., & Sellgren, A. (1998). The Effect of Wear on the Performance of Centrifugal Slurry Pumps. Proceedings of ASME Fluids Engineering Division Summer Meeting. Washington.
Atay, F. (2000). Magnetic Saturation and Steady-State Analysis of Electrical Motor. Applied Mathematical Modelling, 24, 827-842.
Bachert, B., Ludwig, G., Stoffel, B., Sirok, B., & Novak, M. (2003). Experimental Investigations Concerning Erosive Aggressiveness Of Cavitation In A Radial Test Pump With The Aid Of Adhesive Copper Films. Fifth International Symposium on Cavitation. Osaka.
Benbouzid, M., Berghout, T., Sarma, N., Djurovic, S., Wu, Y., & Ma, X. (2021). Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review. MDPI: Energies, 14(5967).
Bross, S., & Addie, G. (2002). Prediction of Impeller Nose Wear Behaviour in Centrifugal Slurry Pumps. Experimental Thermal and Fluid Science, 26, 841-849.
Celik, I. B. (2005). Journal of Fluids Engineering Editorial Policy Statement on the Control of Numerical Accuracy. Morgantown: West Virginia University.
Chowdhury, S., Ali, F., & Jennions, I. (2023). Development of a Novel Ground Test Facility for Aircraft Environmental Control System. ASME Journal of Thermal Science and Engineering Applications, 15.
D'Agostino, L. (2011). Turbomachinery Developments in Cavitation. Pisa: Nato.
Dessena, G., Ignatyev, D., Whidborne, J., Pontillo, A., & Fragonara, L. (2022). Ground Vibration Testing of a Flexible Wing: A Benchmark and Case Study. MDPI - Aerospace, 9(8).
Deyou, L., Hongjie, W., Gaoming, X., Ruzhi, G., Xianzhu, W., & Zhansheng, L. (2015). Unsteady simulation and analysis for hump characteristics of a pump turbine model. Renewable Energy, 77, 32-42.
Dular, M., & Coutier-Degosha, O. (2009). Numerical Modelling of Cavitation Erosion. International Journal for Numerical Methods in Fluids, 61, 1388-1410.
Dular, M., Bachert, B., Stoffel, B., & Sirok, B. (2004). Relationship between Cavitation Structures and Cavitation Damage. Wear, 257, 1176-1184.
Dular, M., Stoffel, B., & Sirok, B. (2006). Development of a Cavitation Erosion Model. Wear, 261, 642–655.
Dular, M., Stoffel, B., & Sirok, B. (2006). Development of a Cavitation Erosion Model. Journal of Wear, 261, 642–655.
Fortes-Patella, R., Reboud, J. L., & Briancon-Marjollet, L. (2004). Aphenomenological And Numerical Model For Scaling The Flowagrssiveness In Cavitation Erosion. Workshop on Cavitation Erosion. Val de Reuil.
Gaydecki, P. (2004). Foundations of Digital Signal Processing: Theory, Algorithms and Hardware Design. London: IET.
Ge, M., Svennberg, U., & Bensow, R. E. (2021). Improved Prediction of Sheet Cavitation Inception Using Bridged Transition Sensitive Turbulence Model and Cavitation Model. Marine Science and Engineering.
Hanusz, Z., & Tarasinska, J. (2011). Tables for Shapiro-Wilk W Statistic According to Royston Approximation. Colloquium Biometricum, 41, 211-219.
Hayward, A. (1979). Flowmeters. London: The MacMillan Press Ltd.
Hughes, A., & Drury, B. (2019). Electric Motors and Drives: Fundamentals, Types and Applications. Oxford: Elsevier.
International Organisation for Standardisation. (2012). ISO 9906:2012 Rotodynamic pumps — Hydraulic performance acceptance tests — Grades 1, 2 and 3. Geneva: ISO.
Jang, S.-M., Choo, H.-W., & Choi, S.-K. (2007). Design and Analysis of a High-Speed Brushless DC Motor for Centrifugal Compressor. IEEE Transactions on Magnetics, 46(6), 2573-2575.
Jiao, X., Huang, B., Li, J., & Xu, G. (2017). Research on Fault Diagnosis of Airborne Fuel Pump Based on EMD and Probablistic Neural Network. Microelectronics Reliability(75), 296-308.
Kahlert, A. (2017). Specification and Evaluation of Prediction Concepts in Aircraft Maintenance. University and State Library Darmstadt, (pp. 1-37). Darmstadt.
Kehtarnavaz, N. (2008). Digital Signal Processing System Design. Amsterdam: Elsevier.
Korkosz, M., Bogusz, P., & Prokop, J. (2018). Modelling and Experimental Research of Fault-Tolerant Dual-Channel Brushless DC Motor. IET Electric Power Applications, 12(6), 787-796.
Li, T., & Van Terswiga, T. (2012). On the capability of a RANS Method to Asses Cavitation Erosion on a Hydrofoil. Proc. of 8th International Symposium on Cavitation. Singapore.
Mao, J. Y., Yuan, S. Q., Pei, J., Zhang, J. F., & Wang, W. J. (2014). Applications of different turbulence models in Applications of different turbulence models in with the diffuser. 27th IAHR Symposium on Hydraulic Machinery and Systems. Montreal.
Maulana, F., Starr, A., & Ompusunggu, A. (2023). Explainable Data-Driven Method Combined with Bayesian Filtering for Remaining Useful Lifetime Prediction of Aircraft Engines Using NASA CMAPSS Datasets. MDPI - Machines, 11(2).
Messina, J., Cooper, P., & Heald, C. (2008). Pump Handbook. McGraw-Hill Publishing.
Mishra, P., Pandey, C., Singh, U., Gupta, A., Sahu, C., & Keshri, A. (2019). Descriptive Statistics and Normality Tests for Statistical Data. Annals of Cardiac Anaesthesia, 22(1), 67-72.
Moody, L. F. (1944). Friction Factors for Pipe Flow. Transactions of the ASME, 66, 671-684.
Niculita, O., Jennions, I., & Irving, P. (2013). Design for Diagnostics and Prognostics: A Physical-Functional Approach. IEEE Aerospace Conference. Big Sky.
Palgrave, R. (2019). VIsual Studies of Cavitation in Pumping Machinery. Texas A&M University Library. Texas.
Rajagopalan, S., Aller, S., Restrepo, J., Habetler, T., & Harley, R. (2006). Detection of Rotor Faults in Brushless DC Motors Operating Under Nonstationary Conditions. IEEE Transactions on Industry Applications.
Rayan, M., & Shawky, M. (1989). Evaluation of Wear in a Centrifugal Slurry Pump. Proceedings of the Institution of Mechanical Engineers. Part A, Journal of Power and Energy.
Sarma, N., Tuohy, P., & Djurovic, S. (2019). Modeling, Analysis, and Validation of Controller Signal Interharmonic Effects in DFIG Drives. IEEE Transaction, 11(2), 713-725.
Sarma, N., Tuohy, P., & Djurovic, S. (2021). Stator Electrical Fault Detection in DFIGs Using Wide-Band Analysis of the Embedded Signals From the Controllers. IEEE Transactions On Energy Conversion, 36(2), 800-811.
Sarma, N., Tuohy, P., Mohammed, A., & Djurovic, S. (2021). Rotor Electrical Fault Detection in DFIGs Using Wide-Band Controller Signals. IEEE Transactions On Sustainable Energy, 12(1), 623-633.
Schlichting, H., & Gersten, K. (2017). Boundary-Layer Theory. Berlin: Springer.
Sedlar, M., Zima, P., Bajorek, M., & Kratky, T. (2012). CFD Analysis of Unsteady Cavitation Phenomena in Multistage Pump with Inducer. IOP Conference Series: Earch and Environmental Science, 2012.
Shapiro, S., & Wilk, M. (1965). An Analysis of Variance Test for Normality (Complete Samples). Biometrika, 52(4), 591-611.
Skaf, Z. (2015). Prognostics: Design, Implementation and Challenges. Twelfth International Conference on Condition Monitoring, 340.
Sreedhar, B., Albert, S., & Pandit, A. (2017). Cavitation Damage: Theory and Measurements. Wear, 177-196.
Taylor, J. (1998). An Introduction to Error Analysis. Sausalita: University Science Books.
Usta, O., Aktas, B., Maasch, M., Osman, T., Atlar, M., & Korekut, E. (2017). A study on the numerical prediction of cavitation erosion for propellers. Symposium of Marine Propulsion (SMP'17). Finland: Strathclyde University.
Verhulst, T. (2023). Health Monitoring Techniques to Diagnose for Cavitation Erosion. Cranfield: Cranfield University.
Verhulst, T., Judt, D., Lawson, M., Chung, Y. M., Al-Tayawe, O., & Ward, G. (2022). Review For State-Of-The-Art Health Monitoring Technologies On Airframe Fuel Pumps. International Journal of Prognostics and Health Management, 13(1).
Verhulst, T., Ng, E., Chung, Y., Judt, D., & Lawson, C. (2022). Predicting Cavitation Erosion on Two-Stage Pumps Using CFD (GT2022-84165). ASME Turbo Expo 2022: Turbomachinery Technical Conference and Exposition. Rotterdam.
Visser, F. C., Dijers, R. J., & op de Woerd, J. H. (2000). Numerical Flow-Field Analysis and Design Optimisation of a High-Energy First-Stage Centrifugal Pump Impeller. Computing and Visualisation in Science, 3, 103-108.
Walker, C. I. (2001). Slurry Pump Side-Liner Wear: Comparison of Some Laboratory and Field Results. Wear, 250, 81-87.
Walker, C. I., & Roudnev, A. (2002). Slurry Pump Impeller Wear: Comparison of Some Laboratory and Field Results. Proceedings of the 15th International Conference of Hydrotransport, (pp. 725-736). Banff.
Wang, Y., Zuo, M., & Fan, X. (2011). Design of An Experimental System for Wear of Slurry Pumps. Proceedings of Canadian Engineering Education Association. Kaninaskis.
Xu, H., Chen, W., & Xu, C. (2019). Cavitation Performance of Multistage Slurry Pump in Deep-Sea Mining. AIP Advances: Fluid and Plasmas Collection, 9, 1-17.
Yilmaz, N., Atlar, M., & Khorasanchi, M. (2019). An improved Mesh Adaption and Refinement approach to Cavitation Simulation (MARCS) of propellers. Ocean Engineering, 171, 139-150.
Ylonen, M. (2016). Cavitation Erosion Characterization of Francis Turbine Runner Blade Material. Tampere: Tampere University of Technology.
Yu, H., & The, J. (2016). Validation and optimization of SST k-u turbulence model for pollutant dispersion within a building array. Atmospheric Environment, 145, 225-238.
Zhang, D. (2017). Comparison of Various Turbulence Models for Unsteady Flow around a Finite Circular Cylinder at Re=20000. The 2017 International Conference on Cloud Technology and Communication Engineering. Guilin.
Zhu, J., & Zhang, H. (2017). Numerical Study on Electrical Submersible Pump Two-Phase Performance and Bubble Size Modelling. Tulsa: University of Tulsa.
Addie, G., & Sellgren, A. (1998). The Effect of Wear on the Performance of Centrifugal Slurry Pumps. Proceedings of ASME Fluids Engineering Division Summer Meeting. Washington.
Atay, F. (2000). Magnetic Saturation and Steady-State Analysis of Electrical Motor. Applied Mathematical Modelling, 24, 827-842.
Bachert, B., Ludwig, G., Stoffel, B., Sirok, B., & Novak, M. (2003). Experimental Investigations Concerning Erosive Aggressiveness Of Cavitation In A Radial Test Pump With The Aid Of Adhesive Copper Films. Fifth International Symposium on Cavitation. Osaka.
Benbouzid, M., Berghout, T., Sarma, N., Djurovic, S., Wu, Y., & Ma, X. (2021). Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review. MDPI: Energies, 14(5967).
Bross, S., & Addie, G. (2002). Prediction of Impeller Nose Wear Behaviour in Centrifugal Slurry Pumps. Experimental Thermal and Fluid Science, 26, 841-849.
Celik, I. B. (2005). Journal of Fluids Engineering Editorial Policy Statement on the Control of Numerical Accuracy. Morgantown: West Virginia University.
Chowdhury, S., Ali, F., & Jennions, I. (2023). Development of a Novel Ground Test Facility for Aircraft Environmental Control System. ASME Journal of Thermal Science and Engineering Applications, 15.
D'Agostino, L. (2011). Turbomachinery Developments in Cavitation. Pisa: Nato.
Dessena, G., Ignatyev, D., Whidborne, J., Pontillo, A., & Fragonara, L. (2022). Ground Vibration Testing of a Flexible Wing: A Benchmark and Case Study. MDPI - Aerospace, 9(8).
Deyou, L., Hongjie, W., Gaoming, X., Ruzhi, G., Xianzhu, W., & Zhansheng, L. (2015). Unsteady simulation and analysis for hump characteristics of a pump turbine model. Renewable Energy, 77, 32-42.
Dular, M., & Coutier-Degosha, O. (2009). Numerical Modelling of Cavitation Erosion. International Journal for Numerical Methods in Fluids, 61, 1388-1410.
Dular, M., Bachert, B., Stoffel, B., & Sirok, B. (2004). Relationship between Cavitation Structures and Cavitation Damage. Wear, 257, 1176-1184.
Dular, M., Stoffel, B., & Sirok, B. (2006). Development of a Cavitation Erosion Model. Wear, 261, 642–655.
Dular, M., Stoffel, B., & Sirok, B. (2006). Development of a Cavitation Erosion Model. Journal of Wear, 261, 642–655.
Fortes-Patella, R., Reboud, J. L., & Briancon-Marjollet, L. (2004). Aphenomenological And Numerical Model For Scaling The Flowagrssiveness In Cavitation Erosion. Workshop on Cavitation Erosion. Val de Reuil.
Gaydecki, P. (2004). Foundations of Digital Signal Processing: Theory, Algorithms and Hardware Design. London: IET.
Ge, M., Svennberg, U., & Bensow, R. E. (2021). Improved Prediction of Sheet Cavitation Inception Using Bridged Transition Sensitive Turbulence Model and Cavitation Model. Marine Science and Engineering.
Hanusz, Z., & Tarasinska, J. (2011). Tables for Shapiro-Wilk W Statistic According to Royston Approximation. Colloquium Biometricum, 41, 211-219.
Hayward, A. (1979). Flowmeters. London: The MacMillan Press Ltd.
Hughes, A., & Drury, B. (2019). Electric Motors and Drives: Fundamentals, Types and Applications. Oxford: Elsevier.
International Organisation for Standardisation. (2012). ISO 9906:2012 Rotodynamic pumps — Hydraulic performance acceptance tests — Grades 1, 2 and 3. Geneva: ISO.
Jang, S.-M., Choo, H.-W., & Choi, S.-K. (2007). Design and Analysis of a High-Speed Brushless DC Motor for Centrifugal Compressor. IEEE Transactions on Magnetics, 46(6), 2573-2575.
Jiao, X., Huang, B., Li, J., & Xu, G. (2017). Research on Fault Diagnosis of Airborne Fuel Pump Based on EMD and Probablistic Neural Network. Microelectronics Reliability(75), 296-308.
Kahlert, A. (2017). Specification and Evaluation of Prediction Concepts in Aircraft Maintenance. University and State Library Darmstadt, (pp. 1-37). Darmstadt.
Kehtarnavaz, N. (2008). Digital Signal Processing System Design. Amsterdam: Elsevier.
Korkosz, M., Bogusz, P., & Prokop, J. (2018). Modelling and Experimental Research of Fault-Tolerant Dual-Channel Brushless DC Motor. IET Electric Power Applications, 12(6), 787-796.
Li, T., & Van Terswiga, T. (2012). On the capability of a RANS Method to Asses Cavitation Erosion on a Hydrofoil. Proc. of 8th International Symposium on Cavitation. Singapore.
Mao, J. Y., Yuan, S. Q., Pei, J., Zhang, J. F., & Wang, W. J. (2014). Applications of different turbulence models in Applications of different turbulence models in with the diffuser. 27th IAHR Symposium on Hydraulic Machinery and Systems. Montreal.
Maulana, F., Starr, A., & Ompusunggu, A. (2023). Explainable Data-Driven Method Combined with Bayesian Filtering for Remaining Useful Lifetime Prediction of Aircraft Engines Using NASA CMAPSS Datasets. MDPI - Machines, 11(2).
Messina, J., Cooper, P., & Heald, C. (2008). Pump Handbook. McGraw-Hill Publishing.
Mishra, P., Pandey, C., Singh, U., Gupta, A., Sahu, C., & Keshri, A. (2019). Descriptive Statistics and Normality Tests for Statistical Data. Annals of Cardiac Anaesthesia, 22(1), 67-72.
Moody, L. F. (1944). Friction Factors for Pipe Flow. Transactions of the ASME, 66, 671-684.
Niculita, O., Jennions, I., & Irving, P. (2013). Design for Diagnostics and Prognostics: A Physical-Functional Approach. IEEE Aerospace Conference. Big Sky.
Palgrave, R. (2019). VIsual Studies of Cavitation in Pumping Machinery. Texas A&M University Library. Texas.
Rajagopalan, S., Aller, S., Restrepo, J., Habetler, T., & Harley, R. (2006). Detection of Rotor Faults in Brushless DC Motors Operating Under Nonstationary Conditions. IEEE Transactions on Industry Applications.
Rayan, M., & Shawky, M. (1989). Evaluation of Wear in a Centrifugal Slurry Pump. Proceedings of the Institution of Mechanical Engineers. Part A, Journal of Power and Energy.
Sarma, N., Tuohy, P., & Djurovic, S. (2019). Modeling, Analysis, and Validation of Controller Signal Interharmonic Effects in DFIG Drives. IEEE Transaction, 11(2), 713-725.
Sarma, N., Tuohy, P., & Djurovic, S. (2021). Stator Electrical Fault Detection in DFIGs Using Wide-Band Analysis of the Embedded Signals From the Controllers. IEEE Transactions On Energy Conversion, 36(2), 800-811.
Sarma, N., Tuohy, P., Mohammed, A., & Djurovic, S. (2021). Rotor Electrical Fault Detection in DFIGs Using Wide-Band Controller Signals. IEEE Transactions On Sustainable Energy, 12(1), 623-633.
Schlichting, H., & Gersten, K. (2017). Boundary-Layer Theory. Berlin: Springer.
Sedlar, M., Zima, P., Bajorek, M., & Kratky, T. (2012). CFD Analysis of Unsteady Cavitation Phenomena in Multistage Pump with Inducer. IOP Conference Series: Earch and Environmental Science, 2012.
Shapiro, S., & Wilk, M. (1965). An Analysis of Variance Test for Normality (Complete Samples). Biometrika, 52(4), 591-611.
Skaf, Z. (2015). Prognostics: Design, Implementation and Challenges. Twelfth International Conference on Condition Monitoring, 340.
Sreedhar, B., Albert, S., & Pandit, A. (2017). Cavitation Damage: Theory and Measurements. Wear, 177-196.
Taylor, J. (1998). An Introduction to Error Analysis. Sausalita: University Science Books.
Usta, O., Aktas, B., Maasch, M., Osman, T., Atlar, M., & Korekut, E. (2017). A study on the numerical prediction of cavitation erosion for propellers. Symposium of Marine Propulsion (SMP'17). Finland: Strathclyde University.
Verhulst, T. (2023). Health Monitoring Techniques to Diagnose for Cavitation Erosion. Cranfield: Cranfield University.
Verhulst, T., Judt, D., Lawson, M., Chung, Y. M., Al-Tayawe, O., & Ward, G. (2022). Review For State-Of-The-Art Health Monitoring Technologies On Airframe Fuel Pumps. International Journal of Prognostics and Health Management, 13(1).
Verhulst, T., Ng, E., Chung, Y., Judt, D., & Lawson, C. (2022). Predicting Cavitation Erosion on Two-Stage Pumps Using CFD (GT2022-84165). ASME Turbo Expo 2022: Turbomachinery Technical Conference and Exposition. Rotterdam.
Visser, F. C., Dijers, R. J., & op de Woerd, J. H. (2000). Numerical Flow-Field Analysis and Design Optimisation of a High-Energy First-Stage Centrifugal Pump Impeller. Computing and Visualisation in Science, 3, 103-108.
Walker, C. I. (2001). Slurry Pump Side-Liner Wear: Comparison of Some Laboratory and Field Results. Wear, 250, 81-87.
Walker, C. I., & Roudnev, A. (2002). Slurry Pump Impeller Wear: Comparison of Some Laboratory and Field Results. Proceedings of the 15th International Conference of Hydrotransport, (pp. 725-736). Banff.
Wang, Y., Zuo, M., & Fan, X. (2011). Design of An Experimental System for Wear of Slurry Pumps. Proceedings of Canadian Engineering Education Association. Kaninaskis.
Xu, H., Chen, W., & Xu, C. (2019). Cavitation Performance of Multistage Slurry Pump in Deep-Sea Mining. AIP Advances: Fluid and Plasmas Collection, 9, 1-17.
Yilmaz, N., Atlar, M., & Khorasanchi, M. (2019). An improved Mesh Adaption and Refinement approach to Cavitation Simulation (MARCS) of propellers. Ocean Engineering, 171, 139-150.
Ylonen, M. (2016). Cavitation Erosion Characterization of Francis Turbine Runner Blade Material. Tampere: Tampere University of Technology.
Yu, H., & The, J. (2016). Validation and optimization of SST k-u turbulence model for pollutant dispersion within a building array. Atmospheric Environment, 145, 225-238.
Zhang, D. (2017). Comparison of Various Turbulence Models for Unsteady Flow around a Finite Circular Cylinder at Re=20000. The 2017 International Conference on Cloud Technology and Communication Engineering. Guilin.
Zhu, J., & Zhang, H. (2017). Numerical Study on Electrical Submersible Pump Two-Phase Performance and Bubble Size Modelling. Tulsa: University of Tulsa.
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Technical Papers