Trouble-free operation of motor-driven valves (MDV) is one of the key factors behind the operating safety of NPPs. As critical components, MDVs are a part of a safety system and a safety-related system. This imposes the highest possible requirements on the MDV reliability. MDVs are the most numerous category of the NPP components. Depending on design, one power unit contains 1500 to 3000 motor-driven valves alone. It follows from an analysis of the NPP failures that many of these are caused by failed motor-driven valves of safety and safety-related systems. The paper presents a description of an automated system for diagnostics of shutoff and control MDVs used in the NPP pipelines. The developed diagnostic algorithms make it possible to take into account the variability of the MDV technical parameters, while taking into account, at the same time, rated restrictions on diagnostic parameters, if any.

Motor operated valves (MOV) are one of the most numerous classes of the nuclear power plant components. An important issue concerned with the MOV diagnostics is the lack of in-process (online) automated control for the MOV technical condition during full power operation of the NPP unit. In this regard, a vital task is that of the MOV diagnostics based on the signals of the current and voltage consumed during MOV ‘opening’ and ‘closing’ operations. The current and voltage signals represent time series measured at regular intervals. The current (and voltage) signals can be received online and contain all necessary information for the online diagnostics of the MOV status. Essentially, the approach allows active power signals to be calculated from the current and voltage signals, and characteristics (‘diagnostic signs’) to be extracted from particular portions (segments) of the active power signals using the values of which MOVs can be diagnosed. The paper deals with the problem of automating the segmentation of active power signals. To accomplish this, an algorithm has been developed based on using a convolutional neural network.

In accordance with Ref. (GOST R 58328-2018 “Pipelines of Nuclear Power Plants. Leak Before Break Concept”), NPPs with VVER-1200 reactors operate an acoustic leak monitoring system (ALMS) and a humidity leak monitoring system (HLMS), each performing the leak monitoring functions locally, independently of the other. The diagnostics results are conveyed to the upper level control system (LCS) to be further displayed for the main control room (MCR) operating personnel. There is also an integrated diagnostics system (IDS) intended to confirm the diagnosis and to update the leak rate values and coordinates based on analyzing the leak monitoring system readings and I&C signals. The system measuring channel readings are composed of background noise, the source for which are processes on the part of the reactor facility’s key components and auxiliary systems, and the leak signal in response to the leak occurrence. A major factor that affects the capability of leak monitoring systems to detect the leak is the quality of the background noise filtering. A new efficient global noise filtering method is proposed for being used as part of the integrated diagnostics system (IDS).

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