Detection of anomalies in reactor equipment operation using neural network algorithms
The problem of detecting anomalies by algorithms based on machine learning methods, in particular, on neural network methods, has recently been very relevant in many industries, including nuclear energy.
The existing system of technical diagnostics of NPP equipment consists in the additional use of rapid diagnostics systems during operation and non-destructive testing tools during planned preventive repairs (PPR).
In this regard, the introduction of predictive analytical systems for the in-depth processing of process control data for the early detection of equipment malfunctions, as well as for the analysis of its resource characteristics, becomes extremely urgent.
The main task of predictive analytics is to build and optimize a digital model to search for anomalies in equipment operation, determine the time interval for equipment trouble-free operation and adjust the scope of maintenance and repair.
The paper presents an algorithm for detecting anomalies in equipment operation, based on the use of neural networks. The efficiency of the developed algorithm was confirmed by the operation of the MCPs at Unit 6 of the Novovoronezh NPP.
The developed algorithm demonstrates high sensitivity to changes in MCP operation modes and makes it possible to control their operation both online and offline.
Implementation of the developed methodology is possible within the framework of the complex diagnostics system (CDS) supplied by JSC «STC Diaprom» to various nuclear power plants.
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