Abstract
This article discusses methods for monitoring and predicting the remaining service life of pumps used in irrigation systems. The relevance of this research is due to the need to improve the reliability and efficiency of pumping equipment, reduce the likelihood of unexpected breakdowns, and optimize maintenance costs. This study aims to create and apply a highly accurate method for predicting the residual service life (RUL) of an irrigation system. The method is based on the use of a convolution neural network. Research Objectives: Study of existing methods for monitoring and predicting the technical condition of pumping equipment. Development of convolutional neural network architecture with two parallel data processing channels: temporal and time-frequency. Creation of the software complex “AITUM” for collection, processing and analysis of data coming from vibration and temperature sensors. Development of recommendations on application of the method in pumping equipment maintenance systems. The constructed method on the basis of deep learning shows high accuracy of prediction of RUL of pumps, which significantly exceeds the generally accepted approaches. A series of experiments on real data of pumping systems were conducted, which helped to discover the degradation patterns of the equipment. Application of this technology will reduce the risk of emergency failures, reduce operating costs and increase the efficiency of irrigation systems through timely maintenance of equipment.