Centrifugal pumps are crucial equipment widely used in various industries, and their reliable operation is essential for maintaining production and preventing costly downtimes. This paper proposes a comprehensive health monitoring system for centrifugal pumps that combines model-driven and data- driven approaches, leveraging artificial intelligence (AI) and machine learning (ML) techniques. The system incorporates demand prediction using artificial neural networks and fault diagnosis employing different ML classifiers, such as decision trees and logistic regression. The model-driven approach involves reconstructing the pump dynamics from vibration signals and analyzing the phase space for fault detection. The data-driven methods utilize support vector machines, k-nearest neighbours, and other algorithms for fault diagnosis based on acquired data. The proposed system aims to provide real-
time, comprehensive information to management, enabling predictive maintenance and minimizing the risk of equipment failure. The paper presents a comparison of different classifiers and health monitoring systems, highlighting the advantages of the proposed comprehensive model. The results demonstrate the effectiveness of the combined decision tree and logistic regression algorithms as a robust foundation for centrifugal pump health monitoring. Additionally, the paper explores the integration of Internet of Things (IoT) and edge computing for real-time monitoring and analysis, addressing challenges such as data security and privacy.

Keywords:Centrifugal Pump, Artificial Intelligence, Machine Learning, Data Acquisition, Internet of
Things

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1. L. Costa Brito, G. Antonio Susto, J. Nei Brito, and M. Antonio Viana Duarte, "AnExplainable Artificial Intelligence Approach for Unsupervised Fault
2. H. M. Chen, J. H. Zhang, Y. C. Wang, H. C. Chang et al., "Hot-Pressing Furnace Current Monitoring and Predictive Maintenance System in Aerospace Applications," 2023.
3. S. F. Chevtchenko, M. C. M. dos Santos, D. M. Vieira, R. L. Mota et al., "PredictiveMaintenance Model Based on Anomaly Detection in Induction Health Monitoring System for Fault Identification Dave Et All
4. Motors: A Machine Learning Approach Using Real-Time IoT Data," 2023.
5. Y. Yang, L. Ding, J. Xiao, G. Fang et al., "Current Status and Applications for HydraulicPump Fault Diagnosis: A Review," 2022.
6. Y. Luo, Y. Han, S. Yuan, and J. Yuan, "Research on the Single-Value Indicators forCentrifugal Pump Based on Vibration Signals," 2020.
7. M. Irfan et al., “A Novel Feature Extraction and Fault Detection Technique for the Intelligent Fault Identification of Water Pump Bearings,” Sensors, vol. 21, no. 12, p. 4225,Jun. 2021, doi: 10.3390/s21124225.
8.  Y. Xiao, Y. Li, and C. Chu, “Performance Analysis of Vibration Sensors for Closed-Loop Feedback Health Monitoring of Mechanical Equipment,” J. Sens., vol. 2021, pp. 1– 12, Oct. 2021, doi: 10.1155/2021/6348347.
9. G. Mousmoulis, N. Karlsen-Davies, G. Aggidis, I. Anagnostopoulos, and D. Papantonis, “Experimental analysis of cavitation in a centrifugal pump using acoustic emission, vibration measurements and flow visualization,” Eur. J. Mech. - BFluids, vol. 75, pp. 300–311, May 2019, doi: 10.1016/j.euromechflu.2018.10.015.
10. M. Karami and R. Madlener, “Smart Predictive Maintenance Strategy Based on Cyber-Physical Systems for Centrifugal Pumps: A Bearing Vibration Analysis,” SSRN Electron. J., 2019, doi: 10.2139/ssrn.3544199.
11.  M. Abd-Elaal, M. Lotfy, T. Nabil, and A. Eldomiaty, “Condition-based monitoring of a small centrifugal pump by vibration analysis,” IOP Conf. Ser. Mater. Sci. Eng., vol. 610, no. 1, p. 012049, Sep. 2019, doi: 10.1088/1757-899X/610/1/012049.
12.  J. S. Rapur and R. Tiwari, “Experimental fault diagnosis for known and unseen operating conditions of centrifugal pumps using MSVM and WPT based analyses,”Measurement, vol. 147, p. 106809, Dec. 2019, doi: 10.1016/j.measurement.2019.07.037.
13.  Z. Ahmad, T.-K. Nguyen, S. Ahmad, C. D. Nguyen, and J.-M. Kim, “Multistage Centrifugal Pump Fault Diagnosis Using Informative Ratio Principal Component Analysis,” Sensors, vol. 22, no. 1, p. 179, Dec. 2021, doi: 10.3390/s22010179.

Centrifugal pumps are crucial equipment widely used in various industries, and their reliable operation is essential for maintaining production and preventing costly downtimes. This paper proposes a comprehensive health monitoring system for centrifugal pumps that combines model-driven and data- driven approaches, leveraging artificial intelligence (AI) and machine learning (ML) techniques. The system incorporates demand prediction using artificial neural networks and fault diagnosis employing different ML classifiers, such as decision trees and logistic regression. The model-driven approach involves reconstructing the pump dynamics from vibration signals and analyzing the phase space for fault detection. The data-driven methods utilize support vector machines, k-nearest neighbours, and other algorithms for fault diagnosis based on acquired data. The proposed system aims to provide real-
time, comprehensive information to management, enabling predictive maintenance and minimizing the risk of equipment failure. The paper presents a comparison of different classifiers and health monitoring systems, highlighting the advantages of the proposed comprehensive model. The results demonstrate the effectiveness of the combined decision tree and logistic regression algorithms as a robust foundation for centrifugal pump health monitoring. Additionally, the paper explores the integration of Internet of Things (IoT) and edge computing for real-time monitoring and analysis, addressing challenges such as data security and privacy.

Keywords:Centrifugal Pump, Artificial Intelligence, Machine Learning, Data Acquisition, Internet of
Things

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. L. Costa Brito, G. Antonio Susto, J. Nei Brito, and M. Antonio Viana Duarte, "AnExplainable Artificial Intelligence Approach for Unsupervised Fault
2. H. M. Chen, J. H. Zhang, Y. C. Wang, H. C. Chang et al., "Hot-Pressing Furnace Current Monitoring and Predictive Maintenance System in Aerospace Applications," 2023.
3. S. F. Chevtchenko, M. C. M. dos Santos, D. M. Vieira, R. L. Mota et al., "PredictiveMaintenance Model Based on Anomaly Detection in Induction Health Monitoring System for Fault Identification Dave Et All
4. Motors: A Machine Learning Approach Using Real-Time IoT Data," 2023.
5. Y. Yang, L. Ding, J. Xiao, G. Fang et al., "Current Status and Applications for HydraulicPump Fault Diagnosis: A Review," 2022.
6. Y. Luo, Y. Han, S. Yuan, and J. Yuan, "Research on the Single-Value Indicators forCentrifugal Pump Based on Vibration Signals," 2020.
7. M. Irfan et al., “A Novel Feature Extraction and Fault Detection Technique for the Intelligent Fault Identification of Water Pump Bearings,” Sensors, vol. 21, no. 12, p. 4225,Jun. 2021, doi: 10.3390/s21124225.
8.  Y. Xiao, Y. Li, and C. Chu, “Performance Analysis of Vibration Sensors for Closed-Loop Feedback Health Monitoring of Mechanical Equipment,” J. Sens., vol. 2021, pp. 1– 12, Oct. 2021, doi: 10.1155/2021/6348347.
9. G. Mousmoulis, N. Karlsen-Davies, G. Aggidis, I. Anagnostopoulos, and D. Papantonis, “Experimental analysis of cavitation in a centrifugal pump using acoustic emission, vibration measurements and flow visualization,” Eur. J. Mech. - BFluids, vol. 75, pp. 300–311, May 2019, doi: 10.1016/j.euromechflu.2018.10.015.
10. M. Karami and R. Madlener, “Smart Predictive Maintenance Strategy Based on Cyber-Physical Systems for Centrifugal Pumps: A Bearing Vibration Analysis,” SSRN Electron. J., 2019, doi: 10.2139/ssrn.3544199.
11.  M. Abd-Elaal, M. Lotfy, T. Nabil, and A. Eldomiaty, “Condition-based monitoring of a small centrifugal pump by vibration analysis,” IOP Conf. Ser. Mater. Sci. Eng., vol. 610, no. 1, p. 012049, Sep. 2019, doi: 10.1088/1757-899X/610/1/012049.
12.  J. S. Rapur and R. Tiwari, “Experimental fault diagnosis for known and unseen operating conditions of centrifugal pumps using MSVM and WPT based analyses,”Measurement, vol. 147, p. 106809, Dec. 2019, doi: 10.1016/j.measurement.2019.07.037.
13.  Z. Ahmad, T.-K. Nguyen, S. Ahmad, C. D. Nguyen, and J.-M. Kim, “Multistage Centrifugal Pump Fault Diagnosis Using Informative Ratio Principal Component Analysis,” Sensors, vol. 22, no. 1, p. 179, Dec. 2021, doi: 10.3390/s22010179.