Abstract
The project is aimed at creating a system for monitoring and forecasting the condition of specialized pumping equipment used in irrigation systems. Such systems use complex and expensive pumps, so ensuring timely and high-quality irrigation of crops requires their uninterrupted operation. The prototype monitoring system was developed using temperature and vibration sensors that were connected to a Raspberry Pi 4 controller. Predictive analysis, feature extraction, and data prepossessing were performed in Python in the Anaconda environment. The study showed that the combined analysis of temperature and vibration parameters helps to quickly detect problems such as misalignment and wear of bearings. Regression models have shown the possibility of predicting equipment failures in advance, and the decomposition of sensor signals has revealed long-term trends and periodic fluctuations. This method can improve the reliability of crop irrigation, significantly reduce downtime, and optimize maintenance schedules