Use of a convolutional neural network to segment signals of motor operated valves

Abstract

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.