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In this paper, it is investigated nuclear power plant operating data which was obtained from reactor main coolant pumps (MCP) of the third isolated generating plant of Kalinin NPP. It is necessary permanent monitoring for state of all pump components since breakdown of a reactor coolant pump leads to substantial economic losses. It is installed over 50 sensors of different control systems at the every MCP. Received data is stored but it is not analysed for the purpose of discovering  joint dependencies between equipment pieces and unobvious, hidden trends of accident propagation. In this work, it was proposed techniques for detection of hidden anomalies and MCP operating regularity based on factor analysis, clustering and linear regression models. It was written a Python script which automates necessary calculations.

Hidden Anomalies Detection in Large Arrays of Nuclear Power Plant Operating Data