Efficiency Monitoring of a Cooling Water Pump based on Machine Learning Techniques
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Published
Jan 22, 2025
Marta Casero
Miguel A. Sanz-Bobi
F. Javier Bellido-López
Antonio Muñoz
Daniel Gonnzalez-Calvo
Tomas Alvarez-Tejedor
Abstract
This paper presents a method for efficiency monitoring of two circulating water pumps working in a combined cycle power plant for cooling the steam coming from a water-steam turbine. The method is based on monitoring the performance of the pumps over time using machine learning techniques that try to discover patterns in the data observed from the pumps. This permits the maintenance staff to assess the possible degradation of the pumps and evaluate the effect of the corrective and preventive maintenance implemented. Some examples of real cases will be presented in the paper to illustrate the method proposed.
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Keywords
machine learning, health condition, cooling water pump, efficiency degradation
References
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Bengtsson, M. & Lundström, G. (2018). On the importance of combining “the new” with “the old” – One important prerequisite for maintenance in Industry 4.0. Procedia Manufacturing, Vol. 25, pp. 118–125.
Bonaccorso, G. Mastering Machine Learning Algorithms (2018). Packt Publishing.
Bowman, C.F., & Bowman, S.N. (2021). Engineering of Power Plant and Industrial Cooling Water Systems. CRC Press. doi: 10.1201/9781003172437
Calvo-Bascones P., Sanz-Bobi M.A. & Welte T.M. (2021). Anomaly detection method based on the deep knowledge behind behavior patterns in industrial components. Application to a hydropower plant. Computers in Industry, Vol. 125, 103376. doi: 10.1016/j.compind.2020.103376.
Calvo-Báscones, P., Sanz-Bobi, M.A., Brighenti, C. & Ricatto, M. (2020). A machine learning method applied to the evaluation of the condition in a fleet of similar vehicles. Proceedings of the European Safety and Reliability Conference and Probabilistic Safety Assessment and Management Conference - ESREL 2020 / PSAM 15, Venice (Italy). 01-05 November.
Chavan, V.D. & Yalagi, P.S. (2023). A Review of Machine Learning Tools and Techniques for Anomaly Detection. In: Choudrie, J., Mahalle, P.N., Perumal, T., Joshi, A. (eds) ICT for Intelligent Systems. ICTIS 2023. Smart Innovation, Systems and Technologies, Vol 361. Springer.
Costa, L. Q. M. D. & Cavalcante, C.A.V. (2022). A review on the study of maintenance effectiveness. Pesquisa Operacional, Vol. 42, no. spe1, p. e263613, doi: 10.1590/0101-7438.2022.042nspe1.00263613
HI, Power Plants Pumps: Guidelines for Application and Operation (2013). Power Plant Pumps Committee, Hydraulic Institute.
Liu, Z., Balieu,R. & Kringos, N. (2022). Integrating sustainability into pavement maintenance effectiveness evaluation: A systematic review. Transportation Research Part D: Transport and Environment, Vol. 104, p. 103187
Maior C. B. S., Araújo L.M.M, Lins I.D., Moura M.D.C. & Droguett E.L. (2023), Prognostics and Health Management of Rotating Machinery via Quantum Machine Learning. IEEE Access, Vol. 11, pp. 25132-25151, doi: 10.1109/ACCESS.2023.3255417.
Moleda, M. Małysiak-Mrozek, B., Ding, W., Sunderam, V. & Mrozek,D. (2023), From Corrective to Predictive Maintenance - A Review of Maintenance Approaches for the Power Industry. Sensors, Vol. 23, no. 13, p. 5970.
Ochella S., Shafiee M. & Dinmohammadi F. Artificial intelligence in prognostics and health management of engineering systems (2022), Engineering Applications of Artificial Intelligence, Vol. 108, 104552, doi: 10.1016/j.engappai.2021.104552.
Orhan N. Predicting deep well pump performance with machine learning methods during hydraulic head changes (2024), Heliyon, Vol. 10, 11, doi: 10.1016/j.heliyon.2024.e31505.
Sinha, P. (2015). Towards higher maintenance effectiveness. International Journal of Quality & Reliability Management, Vol. 32, no. 7, pp. 754–762, doi:10.1108/IJQRM-03-2013-0039
Sunal, C.E., Dyo V. & Velisavljevic, V. Review of machine learning based fault detection for centrifugal pump induction motors (2022). IEEE Access 10: 71344-71355.
Zio, E. (2022). Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice. Reliability Engineering & System Safety, Vol. 218, Part A, pp. 108119 doi: 10.1016/j.ress.2021.108119.
Bengtsson, M. & Lundström, G. (2018). On the importance of combining “the new” with “the old” – One important prerequisite for maintenance in Industry 4.0. Procedia Manufacturing, Vol. 25, pp. 118–125.
Bonaccorso, G. Mastering Machine Learning Algorithms (2018). Packt Publishing.
Bowman, C.F., & Bowman, S.N. (2021). Engineering of Power Plant and Industrial Cooling Water Systems. CRC Press. doi: 10.1201/9781003172437
Calvo-Bascones P., Sanz-Bobi M.A. & Welte T.M. (2021). Anomaly detection method based on the deep knowledge behind behavior patterns in industrial components. Application to a hydropower plant. Computers in Industry, Vol. 125, 103376. doi: 10.1016/j.compind.2020.103376.
Calvo-Báscones, P., Sanz-Bobi, M.A., Brighenti, C. & Ricatto, M. (2020). A machine learning method applied to the evaluation of the condition in a fleet of similar vehicles. Proceedings of the European Safety and Reliability Conference and Probabilistic Safety Assessment and Management Conference - ESREL 2020 / PSAM 15, Venice (Italy). 01-05 November.
Chavan, V.D. & Yalagi, P.S. (2023). A Review of Machine Learning Tools and Techniques for Anomaly Detection. In: Choudrie, J., Mahalle, P.N., Perumal, T., Joshi, A. (eds) ICT for Intelligent Systems. ICTIS 2023. Smart Innovation, Systems and Technologies, Vol 361. Springer.
Costa, L. Q. M. D. & Cavalcante, C.A.V. (2022). A review on the study of maintenance effectiveness. Pesquisa Operacional, Vol. 42, no. spe1, p. e263613, doi: 10.1590/0101-7438.2022.042nspe1.00263613
HI, Power Plants Pumps: Guidelines for Application and Operation (2013). Power Plant Pumps Committee, Hydraulic Institute.
Liu, Z., Balieu,R. & Kringos, N. (2022). Integrating sustainability into pavement maintenance effectiveness evaluation: A systematic review. Transportation Research Part D: Transport and Environment, Vol. 104, p. 103187
Maior C. B. S., Araújo L.M.M, Lins I.D., Moura M.D.C. & Droguett E.L. (2023), Prognostics and Health Management of Rotating Machinery via Quantum Machine Learning. IEEE Access, Vol. 11, pp. 25132-25151, doi: 10.1109/ACCESS.2023.3255417.
Moleda, M. Małysiak-Mrozek, B., Ding, W., Sunderam, V. & Mrozek,D. (2023), From Corrective to Predictive Maintenance - A Review of Maintenance Approaches for the Power Industry. Sensors, Vol. 23, no. 13, p. 5970.
Ochella S., Shafiee M. & Dinmohammadi F. Artificial intelligence in prognostics and health management of engineering systems (2022), Engineering Applications of Artificial Intelligence, Vol. 108, 104552, doi: 10.1016/j.engappai.2021.104552.
Orhan N. Predicting deep well pump performance with machine learning methods during hydraulic head changes (2024), Heliyon, Vol. 10, 11, doi: 10.1016/j.heliyon.2024.e31505.
Sinha, P. (2015). Towards higher maintenance effectiveness. International Journal of Quality & Reliability Management, Vol. 32, no. 7, pp. 754–762, doi:10.1108/IJQRM-03-2013-0039
Sunal, C.E., Dyo V. & Velisavljevic, V. Review of machine learning based fault detection for centrifugal pump induction motors (2022). IEEE Access 10: 71344-71355.
Zio, E. (2022). Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice. Reliability Engineering & System Safety, Vol. 218, Part A, pp. 108119 doi: 10.1016/j.ress.2021.108119.
Section
Technical Briefs