Forecasting the state of equipment

The research area focuses on the development of methods for predicting the technical condition of power system equipment throughout its entire lifecycle, including current condition assessment, identification of parameter change trends, and estimation of failure probability.

The research activities involve the development of mathematical and intelligent models that enable analysis of accumulated equipment operation data, diagnostic measurement results, and operational parameters of energy facilities. Particular attention is given to methods for assessing current equipment condition, predicting defect development, and estimating remaining useful life.

One of the key research directions is the application of artificial intelligence, machine learning, statistical analysis, and digital modeling methods for developing predictive models of equipment technical condition. These approaches enable a transition from traditional schedule-based maintenance to predictive maintenance based on the actual condition of equipment.

The research includes the development of reliability prediction algorithms for power system components, equipment degradation models, remaining useful life assessment methods, and intelligent decision-support systems for maintenance and repair planning.

The obtained results are aimed at improving the reliability of energy facility operation, reducing the probability of equipment failures, optimizing maintenance costs, and developing intelligent lifecycle management systems for power system equipment.