Development of fault detection system in irrigation pumping systems using machine learning methods with consideration of energy and water consumption
Zholdangarova G. Wójcik W.
2025Polska Akademia Nauk
International Journal of Electronics and Telecommunications
2025#71Issue 3
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.
earing fault , normalization , particle swarm optimization , time series , vibration signal
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L. N. Gumilev Eurasian National University, Astana, Kazakhstan
Lublin University of Technology, Lublin, Poland
L. N. Gumilev Eurasian National University
Lublin University of Technology
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026