{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T10:09:18Z","timestamp":1784887758030,"version":"3.55.0"},"reference-count":37,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,3,9]],"date-time":"2022-03-09T00:00:00Z","timestamp":1646784000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Centrifugal pumps have a wide range of applications in industrial and municipal water affairs. During the use of centrifugal pumps, failures such as bearing wear, blade damage, impeller imbalance, shaft misalignment, cavitation, water hammer, etc., often occur. It is of great importance to use smart sensors and digital Internet of Things (IoT) systems to monitor the real-time operating status of pumps and predict potential failures for achieving predictive maintenance of pumps and improving the intelligence level of machine health management. Firstly, the common fault forms of centrifugal pumps and the characteristics of vibration signals when a fault occurs are introduced. Secondly, the centrifugal pump monitoring IoT system is designed. The system is mainly composed of wireless sensors, wired sensors, data collectors, and cloud servers. Then, the microelectromechanical system (MEMS) chip is used to design a wireless vibration temperature integrated sensor, a wired vibration temperature integrated sensor, and a data collector to monitor the running state of the pump. The designed wireless sensor communicates with the server through Narrow Band Internet of Things (NB-IoT). The output of the wired sensor is connected to the data collector, and the designed collector can communicate with the server through 4G communication. Through cloud-side collaboration, real-time monitoring of the running status of centrifugal pumps and intelligent diagnosis of centrifugal pump faults are realized. Finally, on-site testing and application verification of the system was conducted. The test results show that the designed sensors and sensor application system can make good use of the centrifugal pump failure mechanism to automatically diagnose equipment failures. Moreover, the diagnostic accuracy rate is above 85% by using the method of wired sensor and collector. As a low-cost and easy-to-implement solution, wireless sensors can also monitor gradual failures well. The research on the sensors and pump monitoring system provides feasible methods and an effective means for the application of centrifugal pump health management and predictive maintenance.<\/jats:p>","DOI":"10.3390\/s22062106","type":"journal-article","created":{"date-parts":[[2022,3,10]],"date-time":"2022-03-10T02:10:35Z","timestamp":1646878235000},"page":"2106","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":65,"title":["Monitoring and Predictive Maintenance of Centrifugal Pumps Based on Smart Sensors"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4827-963X","authenticated-orcid":false,"given":"Lei","family":"Chen","sequence":"first","affiliation":[{"name":"School of Mechanical and Power Engineering, Zhengzhou University, No. 100 Science Street, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lijun","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Mechanical and Power Engineering, Zhengzhou University, No. 100 Science Street, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mechanical and Power Engineering, Zhengzhou University, No. 100 Science Street, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junshuo","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mechanical and Power Engineering, Zhengzhou University, No. 100 Science Street, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenlong","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mechanical and Power Engineering, Zhengzhou University, No. 100 Science Street, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4040","DOI":"10.1016\/j.eswa.2009.10.002","article-title":"Vibration Based Fault Diagnosis of Monoblock Centrifugal Pump Using Decision Tree","volume":"37","author":"Sakthivel","year":"2010","journal-title":"Expert Syst. Appl."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"106809","DOI":"10.1016\/j.measurement.2019.07.037","article-title":"Experimental Fault Diagnosis for Known and Unseen Operating Conditions of Centrifugal Pumps Using MSVM and WPT Based Analyses","volume":"147","author":"Rapur","year":"2019","journal-title":"Measurement"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"107952","DOI":"10.1016\/j.measurement.2020.107952","article-title":"On the Use of Artificial Neural Networks for Condition Monitoring of Pump-Turbines with Extended Operation","volume":"163","author":"Zhao","year":"2020","journal-title":"Measurement"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Mobley, R.K. (2002). 4\u2014Benefits of Predictive Maintenance. An Introduction to Predictive Maintenance, Butterworth-Heinemann. [2nd ed.]. Plant Engineering.","DOI":"10.1016\/B978-075067531-4\/50004-X"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1016\/j.ymssp.2013.06.004","article-title":"Prognostics and Health Management Design for Rotary Machinery Systems\u2014Reviews, Methodology and Applications","volume":"42","author":"Lee","year":"2014","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1016\/j.ifacol.2019.11.226","article-title":"Decision Making in Predictive Maintenance: Literature Review and Research Agenda for Industry 4.0","volume":"52","author":"Bousdekis","year":"2019","journal-title":"IFAC-PapersOnLine"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1478","DOI":"10.1016\/j.asoc.2012.06.020","article-title":"A Flexible Algorithm for Fault Diagnosis in a Centrifugal Pump with Corrupted Data and Noise Based on ANN and Support Vector Machine with Hyper-Parameters Optimization","volume":"13","author":"Azadeh","year":"2013","journal-title":"Appl. Soft Comput."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Orr\u00f9, P.F., Zoccheddu, A., Sassu, L., Mattia, C., Cozza, R., and Arena, S. (2020). Machine Learning Approach Using MLP and SVM Algorithms for the Fault Prediction of a Centrifugal Pump in the Oil and Gas Industry. Sustainability, 12.","DOI":"10.3390\/su12114776"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"107694","DOI":"10.1016\/j.ymssp.2021.107694","article-title":"Faults Detection and Classification in a Centrifugal Pump from Vibration Data Using Markov Parameters","volume":"158","author":"Fruett","year":"2021","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ahmad, Z., Nguyen, T.-K., Ahmad, S., Nguyen, C.D., and Kim, J.-M. (2022). Multistage Centrifugal Pump Fault Diagnosis Using Informative Ratio Principal Component Analysis. Sensors, 22.","DOI":"10.3390\/s22010179"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1016\/j.renene.2020.08.104","article-title":"Performance Improvement of Mixed-Flow Centrifugal Pumps with New Impeller Shrouds: Numerical and Experimental Investigations","volume":"163","author":"Bozorgasareh","year":"2021","journal-title":"Renew. Energy"},{"key":"ref_12","unstructured":"McKee, K.K., Forbes, G.L., Mazhar, M., Entwistle, R., and Howard, I. (2011, January 16). A Review of Major Centrifugal Pump Failure Modes with Application to the Water Supply and Sewerage Industries. Proceedings of the ICOMS Asset Management Conference Proceedings, Gold Coast, QLD, Australia."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1631\/jzus.A1900608","article-title":"Internal Flow Structure, Fault Detection, and Performance Optimization of Centrifugal Pumps","volume":"21","author":"Tong","year":"2020","journal-title":"J. Zhejiang Univ.-Sci. A"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1007\/s40313-021-00806-w","article-title":"Fast Detection of Centrifugal Pumps Condition by Structural Analysis of MEMS Sensor Signals","volume":"33","author":"Rocha","year":"2022","journal-title":"J. Control Autom. Electr. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Lee, J., Ni, J., Sarangapani, J., and Mathew, J. (2014). A Review of Machinery Diagnostics and Prognostics Implemented on a Centrifugal Pump. Proceedings of the Engineering Asset Management 2011, Springer.","DOI":"10.1007\/978-1-4471-4993-4"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Catelani, M., Ciani, L., Bartolini, A., Del Rio, C., Guidi, G., and Patrizi, G. (2021). Reliability Analysis of Wireless Sensor Network for Smart Farming Applications. Sensors, 21.","DOI":"10.3390\/s21227683"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Sunny, A.I., Zhao, A., Li, L., and Kanteh Sakiliba, S. (2021). Low-Cost IoT-Based Sensor System: A Case Study on Harsh Environmental Monitoring. Sensors, 21.","DOI":"10.3390\/s21010214"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Abraham, M.T., Satyam, N., Pradhan, B., and Alamri, A.M. (2020). IoT-Based Geotechnical Monitoring of Unstable Slopes for Landslide Early Warning in the Darjeeling Himalayas. Sensors, 20.","DOI":"10.3390\/s20092611"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ren, S., Kim, J.-S., Cho, W.-S., Soeng, S., Kong, S., and Lee, K.-H. (2021, January 13\u201316). Big Data Platform for Intelligence Industrial IoT Sensor Monitoring System Based on Edge Computing and AI. Proceedings of the 2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), Jeju Island, Korea.","DOI":"10.1109\/ICAIIC51459.2021.9415189"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Vaclavova, A., Strelec, P., Horak, T., Kebisek, M., Tanuska, P., and Huraj, L. (2022). Proposal for an IIoT Device Solution According to Industry 4.0 Concept. Sensors, 22.","DOI":"10.3390\/s22010325"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"58052","DOI":"10.1109\/ACCESS.2021.3072854","article-title":"A Fault Diagnosis Framework for Centrifugal Pumps by Scalogram-Based Imaging and Deep Learning","volume":"9","author":"Hasan","year":"2021","journal-title":"IEEE Access"},{"key":"ref_22","first-page":"e6103947","article-title":"Prediction Model of Vibration Feature for Equipment Maintenance Based on Full Vector Spectrum","volume":"2017","author":"Chen","year":"2017","journal-title":"Shock. Vib."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"115109","DOI":"10.1088\/1361-6501\/ac1181","article-title":"Numerical Method to Predict Vibration Characteristics Induced by Cavitation in Centrifugal Pumps","volume":"32","author":"Cao","year":"2021","journal-title":"Meas. Sci. Technol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"012139","DOI":"10.1088\/1742-6596\/364\/1\/012139","article-title":"Diagnosis of Centrifugal Pump Faults Using Vibration Methods","volume":"364","author":"AlThobiani","year":"2012","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"105246","DOI":"10.1016\/j.engfailanal.2021.105246","article-title":"Failure Analysis of Fractured Motor Bolts in High-Speed Train Due to Cardan Shaft Misalignment","volume":"122","author":"Hu","year":"2021","journal-title":"Eng. Fail. Anal."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"5536853","DOI":"10.1155\/2021\/5536853","article-title":"Study on the Unbalanced Fault Dynamic Characteristics of Eccentric Motorized Spindle Considering the Effect of Magnetic Pull","volume":"2021","author":"Wang","year":"2021","journal-title":"Shock. Vib."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1006\/jsvi.2000.3341","article-title":"Stability and Nonlinear Responses of a Rotor-Bearing System with Pedestal Looseness","volume":"241","author":"Chu","year":"2001","journal-title":"J. Sound Vib."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhou, T., Huang, F., Han, Y., and Luo, Y. (2021, January 15\u201317). Study on Dynamics Characteristics of Labyrinth-Seal-Rotor System with Pedestal Looseness. Proceedings of the 2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing), Nanjing, China.","DOI":"10.1109\/PHM-Nanjing52125.2021.9612835"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"012127","DOI":"10.1088\/1757-899X\/1030\/1\/012127","article-title":"Calculation of Water Hammer on the Pressure Pipeline of Modernized Irrigation Pumping Station","volume":"1030","author":"Kan","year":"2021","journal-title":"IOP Conf. Ser. Mater. Sci. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"6767","DOI":"10.1177\/09544062211000768","article-title":"Dynamic Interaction between Valve-Closure Water Hammer Wave and Centrifugal Pump","volume":"235","author":"Zhang","year":"2021","journal-title":"Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"108676","DOI":"10.1016\/j.measurement.2020.108676","article-title":"Blockage and Cavitation Detection in Centrifugal Pumps from Dynamic Pressure Signal Using Deep Learning Algorithm","volume":"173","author":"Tiwari","year":"2021","journal-title":"Measurement"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1007\/s40799-020-00362-z","article-title":"Detection of Cavitation Phenomenon within a Centrifugal Pump Based on Vibration Analysis Technique in Both Time and Frequency Domains","volume":"44","year":"2020","journal-title":"Exp. Tech."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Guo, C., Gao, M., and He, S. (2020). A Review of the Flow-Induced Noise Study for Centrifugal Pumps. Appl. Sci., 10.","DOI":"10.3390\/app10031022"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.icte.2017.03.004","article-title":"A Survey on LPWA Technology: LoRa and NB-IoT","volume":"3","author":"Sinha","year":"2017","journal-title":"ICT Express"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Yogendra Prasad, P., Heera, H., Padmaja, N., and Sony, B. (2021). A Comparative Analysis of Long Range and NB-IoT in Terms of Quality of Connectivity. Mater. Today Proc., 1\u201310.","DOI":"10.1016\/j.matpr.2020.12.350"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Losada, D.E., and Fern\u00e1ndez-Luna, J.M. (2005, January 21\u201323). A Probabilistic Interpretation of Precision, Recall and F-Score, with Implication for Evaluation. Proceedings of the Advances in Information Retrieval, Santiago de Compostela, Spain.","DOI":"10.1007\/b107096"},{"key":"ref_37","unstructured":"Flach, P., and Kull, M. (2015, January 7\u201312). Precision-Recall-Gain Curves: PR Analysis Done Right. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/6\/2106\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:33:28Z","timestamp":1760135608000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/6\/2106"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,9]]},"references-count":37,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["s22062106"],"URL":"https:\/\/doi.org\/10.3390\/s22062106","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,9]]}}}