Development of Fault Detection System in Irrigation Pumping Systems Using Machine Learning Methods with Consideration of Energy and Water Consumption

Aug 4, 2026

Pumping systems are vital in agriculture for delivering the irrigation levels needed to increase crop yields. Pump malfunctions lead to equipment downtime, lower operational efficiency, and substantial financial losses, making early fault detection and diagnosis a high-impact applied research goal. The study develops and validates early fault detection and classification approaches for irrigation pumping systems using advanced machine learning algorithms combined with sensor data analysis, including techniques for sensor analytics and filtering.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages6

Reading Time15 min

AccessFree to download

Introduction

Diagnostic methods for pumping systems

Machine learning and sensor-based monitoring

Common pump faults and condition monitoring approaches

Experimental Setup

Dataset requirements

Experimental platform design

Instrumentation and operating conditions

INTL JOURNAL OF ELECTRONICS AND TELECOMMUNICATIONS, 2025, VOL. 71, NO. 3, PP. 1-6 Manuscript received June 25, 2025; revised July 2025. doi: 10.24425/ijet.2025.153635 Development of fault detection system in irrigation pumping systems using machine learning methods with consideration of energy and water consumption Gulnar Zholdangarova, and Waldemar Wójcik Abstract—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. Keywords—vibration signal; time series; earing fault; particleswarm optimization; normalization I. INTRODUCTION TRADITIONAL diagnostic methods for pumping systems are based on manual and regular inspections. Vibration analysis [1], temperature analysis [2], wear analysis, motor current signature analysis [3] and acoustic emission analysis [3] have been the basis of many diagnostic methods. Vibration analysis is considered the most effective of these methods because it can provide significant information about anomalies. Their main drawbacks include low accuracy, inability to detect hidden issues in a timely manner, and heavy reliance on manual labor. By contrast, modern diagnostic methods provide adaptive filtering, feature extraction, and model training on large amounts of sensory data. Modern machine learning methods are automated and accurate, but require large volumes of labeled data and complex infrastructure for processing Faults in irrigation systems are mainly detected through periodic manual inspections or basic monitoring based on threshold values. Predicting and monitoring maintenance in real time has been made possible by recent developments in machine learning and sensor technologies [5] . The introduction of the Internet of Things (IoT) enhances remote 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” Gulnar Zholdangarova is with L. N. Gumilev Eurasian National University, Astana, Kazakhstan (e-mail: [zholdangarova9443@gmail.com](zholdangarova9443@gmail.com)). Waldemar Wójcik is with Lublin University of Technology, Lublin, Poland ([e-mail: waldemar.wojcik@pollub.pl](e-mail: waldemar.wojcik@pollub.pl)) monitoring capabilities by facilitating the real-time transfer of sensor data to centralized systems. Sensor fusion techniques that combine data from multiple sensors will improve diagnostic accuracy. For example, high diagnostic accuracy can be achieved by fusing data from vibration, pressure, and temperature sensors [6] . However, noise and data redundancy remain significant challenges in sensor deployment. The application of dimensional reduction techniques such as PCAis necessary due to the large amount of data from different sensors [7] . Irrigation pumping systems utilize several sensors to monitor operating parameters. These include temperature, pressure, vibration and flow sensors. For example, studies have shown that vibration data is critical for detecting mechanical faults, and they have shown that the accuracy of fault detection using neural networks is 93 percent [8] . To monitor environmental conditions that affect pump performance, temperature and humidity sensors are also needed [9] . Leaks, clogs, pump wear and vibrat

FAQs

Why is early fault detection important for irrigation pumping systems?

Pump malfunctions cause equipment downtime, reduced efficiency of agricultural production, and significant financial losses. Early detection helps prevent these impacts by identifying faults sooner.

What sensors and data are considered for diagnosing pump faults?

The document highlights temperature, pressure, vibration, and flow sensors, noting that vibration data is especially effective for mechanical anomaly detection. Sensor fusion and dimensional reduction (e.g., PCA) are discussed to improve accuracy and handle large data volumes.

How does the research approach fault detection using machine learning?

It targets early fault detection and classification using advanced machine learning algorithms alongside sensor data analysis. The setup emphasizes data quality and the use of experimental datasets to train and validate the models.

Aug 4, 2026

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