Parameter Autoregression Algorithm-Based Early Warning Method for Critical Equipment in Nuclear Power Plants

A methodology based on the parameter autoregression algorithm is designed and developed for the early warning of critical equipment in nuclear power plants. The method innovatively introduces a parameter autoregression algorithm based on multi-dimensional time sequence data, which estimates the parameters under normal operation of the equipment and extracts the residual characteristics by comparing them with the measured values, thus realizing a dynamic threshold-based equipment condition monitoring mechanism. In addition, combined with the equipment mechanism, this study proposes and adopts the key concept of measurement point importance, and through the modeling of the core components of the equipment, the identification of the equipment operating state, the early warning of abnormal signs, the identification of faulty components and the generation of relevant alarm events are achieved. This study tests and validates the designed and developed method on the reactor coolant pump (hereinafter referred to as the main pump), the core equipment of AP1000 nuclear power unit. Through the analysis of the actual operation data and abnormal events of the main pump, compared with the existing equipment condition monitoring methods, the newly established early warning method for critical equipment can detect abnormal signs of relevant equipment at an early stage, produce early warning, and provide key information to assist engineers in fault analysis and localization, thus significantly shortening the time for fault diagnosis and troubleshooting, and greatly reducing the manpower input for critical equipment monitoring.