The article deals with the results of empirical modeling of the oil system of the main circulation pumps of a nuclear power plant, designed for oil supply to support bearings and their cooling. The empirical model extends the industrial monitoring platform with a sliding linear predictor to maintain the operational safety and operability of the MCP. The initial data for the predictor are the controlled parameters of the MCP.
The article deals with the one-group approximation to the problem of parametric identification of the distribution of isotropic sources, providing the required configuration of the neutron field in vacuum.
The article presents the results of identifying pre-failure conditions. The results based on fractal analysis and nonparametric statistics. The NPP equipment units are highly reliable systems for long life cycle. These systems are characterized by slow graduating failures. This happens due to the accumulation of irreversible damage. Standard information measuring systems supply time series. They are traditionally processed by parametric methods. The processing of experimental data can be automated for industrial monitoring of NPP equipment parameters.
The article deals with the concept of monitoring based on nonparametric statistics. It is supposed the possible application to monitor the technological systems of nuclear power plants. Modern complex plants and technogenic objects are equipped with industrial monitoring systems. The evolution of such systems in natural form is the generation of alternative concepts, constructs, and items selection for efficiency during operation
