{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T11:22:34Z","timestamp":1788434554528,"version":"build-2803163510"},"reference-count":42,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T00:00:00Z","timestamp":1788393600000},"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>Cavitation is one of the major factors reducing the performance, efficiency, and service life of deep well submersible pumps used in agricultural irrigation systems. Therefore, the early detection of cavitation is essential for improving pump reliability and reducing maintenance costs. In this study, a machine learning-based framework is proposed to detect and classify cavitation conditions using experimental data collected from a deep well pump test unit. Hydraulic and operational parameters were measured under different operating conditions, while the measured noise level was used only to assign cavitation labels during dataset preparation. According to the measured noise level, the operating conditions were classified into three categories: Normal, Incipient Cavitation, and Severe Cavitation. Several machine learning algorithms were evaluated using stratified cross-validation and an independent test dataset. Model performance was assessed using Accuracy, Precision, Recall, and F1-score. The results showed that the Extra Tree classifier achieved the best performance with an accuracy of 82.4%. Feature importance analysis indicated that power consumption and submergence depth were the most influential parameters for cavitation detection. Unlike many existing studies that rely on computationally intensive models, the proposed framework employs a simple and lightweight machine learning approach while maintaining reliable prediction performance. Its low computational complexity makes it a promising candidate for future implementation on resource-constrained edge devices, enabling real-time cavitation monitoring in agricultural pumping systems.<\/jats:p>","DOI":"10.3390\/s26175596","type":"journal-article","created":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T11:08:53Z","timestamp":1788433733000},"page":"5596","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Lightweight Machine Learning Framework for Cavitation Detection in Submersible Pumps Using Experimental Multi-Sensor Data"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7097-2521","authenticated-orcid":false,"given":"Seyit Alperen","family":"Celtek","sequence":"first","affiliation":[{"name":"Department of Electric Electronic Engineering, Karamanoglu Mehmetbey University, 70100 Karaman, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"}]},{"given":"Seyma","family":"Sattuf","sequence":"additional","affiliation":[{"name":"Department of Energy Systems Engineering, Karamanoglu Mehmetbey University, 70100 Karaman, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8434-0525","authenticated-orcid":false,"given":"Farhad","family":"Shahnia","sequence":"additional","affiliation":[{"name":"School of Engineering and Energy, Murdoch University, Perth, WA 6150, Australia"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"investigation","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"}]},{"given":"Nuri","family":"Orhan","sequence":"additional","affiliation":[{"name":"Department of Agricultural Machinery and Technology Engineering, Selcuk University, 42100 Konya, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1007\/s43832-026-00354-x","article-title":"Smart water for sustainable agriculture through climate resilient assessment and integrated soil water crop management","volume":"6","author":"AbdelRahman","year":"2026","journal-title":"Discov. Water"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"20445","DOI":"10.1007\/s13369-025-10536-9","article-title":"A Comprehensive Review of Failure Modes in Electrical Submersible Pumps: Diagnosis, Predictive Maintenance, and Engineer\u2019s Guide","volume":"50","author":"Khalili","year":"2025","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"163","DOI":"10.18100\/ijamec.804049","article-title":"Submersible Pump Vortex Detection Using Image Processing Technique and Neuro-Fuzzy","volume":"8","author":"Durdu","year":"2020","journal-title":"Int. J. Appl. Math. Electron. Comput."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Orhan, N., and Kaya, E. (2026). Real-time optimization of submersible pump performance using artificial neural networks and shiny interface. Proc. Inst. Mech. Eng. Part E J. Process Mech. Eng., 09544089261432277.","DOI":"10.1177\/09544089261432277"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"041301","DOI":"10.1063\/5.0315660","article-title":"Vapor bubble growth rate models for three cavitation regimes: Review and model correction","volume":"38","author":"Li","year":"2026","journal-title":"Phys. Fluids"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"103043","DOI":"10.1016\/j.flowmeasinst.2025.103043","article-title":"Performance optimization of centrifugal pumps: Experimental analysis of flow enhancement and cavitation mitigation under variable operating conditions","volume":"106","author":"Ebada","year":"2025","journal-title":"Flow. Meas. Instrum."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ljubojevi\u0107, P., Lazovi\u0107, T., and Doj\u010dinovi\u0107, M. (2026). Cavitation in Machine Elements: A Critical Review of Cavitation Damage, Experimental Methods, Standardization Challenges, and Applied Digital Technologies. Lubricants, 14.","DOI":"10.3390\/lubricants14060237"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"136515","DOI":"10.1016\/j.energy.2025.136515","article-title":"Numerical simulation and experimental study on cavitation and pressure fluctuation characteristics of low head pumped storage system under pump operating conditions","volume":"328","author":"Jiao","year":"2025","journal-title":"Energy"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Yang, L., Liu, Q., Xia, T., Ye, C., and Li, J. (2022). Preventive maintenance strategy optimization in manufacturing system considering energy efficiency and quality cost. Energies, 15.","DOI":"10.3390\/en15218237"},{"key":"ref_10","first-page":"200501","article-title":"Systematic review of predictive maintenance practices in the manufacturing sector","volume":"26","author":"Benhanifia","year":"2025","journal-title":"Intell. Syst. Appl."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"20275","DOI":"10.1109\/JIOT.2026.3660780","article-title":"AI-driven predictive maintenance in industrial IoTs: A comprehensive survey","volume":"13","author":"Khan","year":"2026","journal-title":"IEEE Internet Things J."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"53","DOI":"10.4028\/p-oO4Sal","article-title":"A Predictive Maintenance Approach for Centrifugal Pumps: Case Study of Vibration Analysis Using FFT and Phase Difference Diagnostics","volume":"933","author":"Riyandwita","year":"2026","journal-title":"Appl. Mech. Mater."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"23545","DOI":"10.1038\/s41598-025-08515-z","article-title":"Comparison of deep learning models for predictive maintenance in industrial manufacturing systems using sensor data","volume":"15","author":"Li","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_14","first-page":"385","article-title":"Integrating Machine Learning and IoT for Real-Time PdM in Industrial Ecosystems: A Case Study Analysis","volume":"14","author":"Sekar","year":"2025","journal-title":"Int. J. Res. Ind. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ma, W., Ma, S., Zou, Z., Fu, B., Ma, J., Liu, J., and Zhang, Q. (2025). Literature review on fault mechanism analysis and diagnosis methods for main pump systems. Machines, 13.","DOI":"10.3390\/machines13111000"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Sarr, A., Chandel, A.K., Lamine Diop, L.D., Soro, Y.M., Tossa, A.K., Hota, S., and Manimozhian, A. (2026). Agroclimatic Sensing, Communication, and Computational Systems-Based Methods and Technologies for Precision Irrigation Management: Current State and Prospects. Computers, 15.","DOI":"10.3390\/computers15020137"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"20339","DOI":"10.1109\/JIOT.2026.3663918","article-title":"IoT service orchestration in edge-cloud continuum with 6G: A review","volume":"13","author":"Shahid","year":"2026","journal-title":"IEEE Internet Things J."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.jnca.2017.08.021","article-title":"Cloud monitoring: A review, taxonomy, and open research issues","volume":"98","author":"Syed","year":"2017","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_19","first-page":"1","article-title":"Enhancing industrial automation and safety through real-time monitoring and control systems","volume":"1","author":"Sharma","year":"2024","journal-title":"Int. J. Smart Sustain. Intell. Comput."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"111252","DOI":"10.1016\/j.compag.2025.111252","article-title":"A comprehensive review of Edge Computing empowered smart agriculture: Trends, Opportunities and Future directions","volume":"241","author":"Gauttam","year":"2026","journal-title":"Comput. Electron. Agric."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Loukatos, D., Kondoyanni, M., Alexopoulos, G., Maraveas, C., and Arvanitis, K.G. (2023). On-device intelligence for malfunction detection of water pump equipment in agricultural premises: Feasibility and experimentation. Sensors, 23.","DOI":"10.3390\/s23020839"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Gacu, J.G., Monjardin, C.E.F., Mangulabnan, R.G.T., Pugat, G.C.E., and Solmerin, J.G. (2025). Artificial intelligence (AI) in surface water management: A comprehensive review of methods, applications, and challenges. Water, 17.","DOI":"10.3390\/w17111707"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Choe, H.-O., and Lee, M.-H. (2025). Artificial Intelligence-Based Anomaly Detection Technology for Equipment Condition Monitoring in Smart Farms. Appl. Sci., 15.","DOI":"10.3390\/app152312843"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"100731","DOI":"10.1109\/ACCESS.2025.3577911","article-title":"An innovative smart irrigation using embedded and regression-based machine learning technologies for improving water security and sustainability","volume":"13","author":"Morchid","year":"2025","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1007\/s13202-025-02070-z","article-title":"Time-aware predictive maintenance of electrical submersible pumps using catboost ensemble learning and trend-based labeling","volume":"15","author":"Khalili","year":"2025","journal-title":"J. Pet. Explor. Prod. Technol."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Celtek, S.A., Polat, A.O., and Kul, S. (2025). Anomaly Detection for Smart Soil Sensors: A Comparative Analysis of Statistical and Machine Learning Methods. Proceedings of the 2025 7th International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency (SUMMA), IEEE.","DOI":"10.1109\/SUMMA68668.2025.11302370"},{"key":"ref_27","first-page":"436","article-title":"An Ensemble Machine Learning Model for Predictive Maintenance on Water Injection Pumps in the Oil and Gas Industry","volume":"15","author":"Almazrouei","year":"2024","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"041005","DOI":"10.1115\/1.4056365","article-title":"Internal leakage detection in hydraulic pump using model-agnostic feature ranking and ensemble classifiers","volume":"23","author":"Prakash","year":"2023","journal-title":"J. Comput. Inf. Sci. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"16878132221089170","DOI":"10.1177\/16878132221089170","article-title":"Condition monitoring of water pump bearings using ensemble classifier","volume":"14","author":"Irfan","year":"2022","journal-title":"Adv. Mech. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"10357","DOI":"10.1038\/s41598-026-40335-7","article-title":"An optimized ensemble framework for machinery fault detection in IoT environments","volume":"16","author":"Kanagaraj","year":"2026","journal-title":"Sci. Rep."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2130","DOI":"10.2118\/218418-PA","article-title":"Hybrid long short-term memory and convolutional neural network architecture for electric submersible pump condition prediction and diagnosis","volume":"29","author":"Liu","year":"2024","journal-title":"SPE J."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"533","DOI":"10.33462\/jotaf.1324561","article-title":"Machine learning-based prediction of NPSH, noise, and vibration levels in radial pumps under cavitation conditions","volume":"21","author":"Orhan","year":"2024","journal-title":"Tekirda\u011f Ziraat Fak. Derg."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Yu, L., and Cheng, L. (2025). Cavitation Coefficient Identification Model for an Axial Flow Pump Based on Pressure Signal Feature Extraction and Spider Wasp Optimization Algorithm. J. Mar. Sci. Eng., 14.","DOI":"10.3390\/jmse14010018"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Sanchez, E., and Busboom, A. (2026). Cavitation Monitoring in Rotating Hydraulic Machines Using Machine Learning\u2014A Review. Appl. Sci., 16.","DOI":"10.20944\/preprints202603.1027.v1"},{"key":"ref_35","unstructured":"(2019). Acoustics\u2014Determination of Sound Power Levels of Noise Sources\u2014Guidelines for the Use of Basic Standards (Standard No. ISO 3740:2019)."},{"key":"ref_36","unstructured":"(2012). Rotodynamic Pumps\u2014Hydraulic Performance Acceptance Tests\u2014Grades 1, 2 and 3 (Standard No. ISO 9906:2012)."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"613","DOI":"10.33462\/jotaf.769037","article-title":"Detection of Vortex Cavitation with the Method Adaptive Neural Fuzzy Networks in the Deep Well Pumps","volume":"18","author":"Durdu","year":"2021","journal-title":"Tekirda\u011f Ziraat Fak. Derg."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.measurement.2018.07.092","article-title":"Prediction of flow blockages and impending cavitation in centrifugal pumps using Support Vector Machine (SVM) algorithms based on vibration measurements","volume":"130","author":"Panda","year":"2018","journal-title":"Measurement"},{"key":"ref_39","first-page":"102919","article-title":"Cavitation diagnosis for water distribution pumps: An early-stage approach combing vibration signal-based neural network with high-speed photography","volume":"55","author":"Tong","year":"2023","journal-title":"Sustain. Energy Technol. Assess."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"105299","DOI":"10.1016\/j.jwpe.2024.105299","article-title":"Multi-cavitation states identification of a sewage pump using CEEMDAN and BOA-SVM","volume":"61","author":"Zhou","year":"2024","journal-title":"J. Water Process Eng."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"129167","DOI":"10.1016\/j.eswa.2025.129167","article-title":"Evaluation of supervised machine learning techniques for cavitation detection and diagnosis in a pump-as-turbine system","volume":"296","author":"Stephen","year":"2025","journal-title":"Expert. Syst. Appl."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1109\/34.908974","article-title":"PCA versus LDA","volume":"23","author":"Martinez","year":"2001","journal-title":"IEEE Trans. Pattern Anal. Mach. 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