Development of fault detection system in irrigation pumping systems using machine learning methods with consideration of energy and water consumption

• Autorzy: Zholdangarova Gulnar , Wójcik Waldemar

Identyfikatory

DOI

10.24425/ijet.2025.153635

• Języki publikacji: EN

Abstrakty

EN

Pumping systems play an important role in agriculture because they provide the necessary level of irrigation needed to increase crop yields. Pump malfunctions result in equipment downtime, reduced efficiency of agricultural production and significant financial losses. Thus, the development of an early fault detection and diagnosis system leveraging sensor analytic, filtering techniques, and machine learning (ML) technologies constitutes a critical applied research challenge. The aim of this research is to develop and validate early fault detection and classification methods for pumping systems using advanced machine learning algorithms and sensor data analysis.

Słowa kluczowe

EN

vibration signal; time series; earing fault; particle swarm optimization; normalization

• Wydawca: Polish Academy of Sciences, Committee of Electronics and Telecommunication
• Czasopismo: International Journal of Electronics and Telecommunications
• Rocznik: 2025
• Tom: Vol. 71, No. 3
• Strony: 13
• Opis fizyczny: Bibliogr. 13 poz., tab., fot., rys.

Twórcy

autor

Zholdangarova Gulnar

• L.N. Gumilev Eurasian National University, Astana, Kazakhstan

autor

Wójcik Waldemar

• Lublin University of Technology, Lublin, Poland

Bibliografia

• [1] Y.Wang, J.Xiang, R.Markert, and M.Liang, “Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: A review with applications,” Mech. Syst. Signal Process.vol. 66, pp. 679-698, Mar. 2016. https://doi.org/10.1016/j.ymssp.2015.04.039
• [2] Y.Lei, B.Yang, X.Jiang, F.Jia, N.Li, and A.K. Nandi, “Applications of machine learning to machine fault diagnosis: A review and roadmap,” Mechanical Systems and Signal Processing, vol. 138, pp.106587, 2020. http://dx.doi.org/10.1016/j.ymssp.2019.106587
• [3] N. Mehala, “Condition Monitoring and Fault Diagnosis of Induction Motor Using Motor Current Signature Analysis,” Electr. Eng.,vol. 2, 175. 2010.
• [4] K.Du, X.Li, M.Tao, and S.Wang, “Experimental study on acoustic emission (AE) characteristics and crack classification during rock fracture in several basic lab tests,” International Journal of Rock Mechanics and Mining Sciences, vol. 133, pp.104411, 2020. https://doi.org/10.1016/j.ijrmms.2020.104411
• [5] T. Chen, F. Dong, H. Wang, Z. Xu, and E. Liu, “Construction of Intelligent Fault Diagnosis System for Pumping Station Based on Fusion Algorithm,” 454-460. (2024).
• [6] G.Chen, L.Wang, H.Yang, P.Wang, J.Wei, and J.Bao, “Health assessment of water pumps using high-dimensional monitoring data,” Water Science & Technology Water Supply, vol. 23(2), 2023. http://dx.doi.org/10.2166/ws.2023.244
• [7] D. Loukatos, M. Kondoyanni, G. Alexopoulos, C. Maraveas, and K. G. Arvanitis, “On-Device Intelligence for Malfunction Detection of Water Pump Equipment in Agricultural Premises: Feasibility and Experimentation,” Sensors, vol. 23(2), 839, 2023. https://doi.org/10.3390/s23020839
• [8] S. Liang, P. Liu, S. Zhang, and Z. Wu, “Research on Fault Diagnosis of Agricultural IoT Sensors Based on Improved Dung Beetle Optimization-Support Vector Machine,” Sustainability, vol. 16(22), pp. 10001, 2024. https://doi.org/10.3390/su162210001
• [9] S. Sahoo, A. Singh, and M. K. N. Kumari, “Identifying Anomalies in Water Pump Systems Using Machine Learning and an Integrated Ensemble Method,” in proc. ICDSIS, pp. 1-6, 17-18 May 2024. http://dx.doi.org/10.1109/ICDSIS61070.2024.10594390
• [10] M. A. Febriantono, N. W. Prasetya, S. Sidharta, “Intelligent irrigation management system based on iot and machine learning,” AIP Conf. Proc., vol. 2927, pp. 040002, 2024. https://doi.org/10.1063/5.0193464
• [11] D.K. Saha, S. Ahmed, and S. Shaurov, “Different Machine Maintenance Techniques of Rotary Machine and Their Future Scopes: A Review,” In Proceedings of the 2019 4th International Conference on Electrical Information and Communication Technology (EICT), Khulna, Bangladesh, pp. 1-6, 20-22 December 2019.
• [12] Q.Jiang,; F.Chang, “A novel rolling-element bearing faults classification method combines lower-order moment spectra and support vector machine,” Journal of Mechanical Science and Technology, vol. 33, pp. 1535-1543, 2019. https://doi.org/10.1007/s12206-019-0305-2
• [13] R. Poli, J. Kennedy, and T. Blackwell, “Particle swarm optimization,” Swarm Intell, vol. 1, pp. 33-57, 2007. https://doi.org/10.1007/s11721-007-0002-0 2007

Uwagi

1. Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).

2. This work was carried out at the expense of grant financing of scientific research for 2024-2026 under the project АР23490529 “Development of information system and mathematical models for monitoring and load forecasting of electric power systems on the basis of hybrid technologies”