The Use of Convolutional Neural Network for Segmenting Signals of Electrically-Actuated Valves
Electrically”actuated valves (EAV) represent one of the most numerous classes of equipment at nuclear power plants. The main problem of diagnosing EAV failures is the lack of operational (online) automated control of the technical condition of the EAV when the power unit is operating at full capacity.
In this regard, an important task is diagnosing the EAV according to the current and voltage signals consumed during the ‘opening’ and ‘closing’ operations of the EAV. The current and voltage signals are time series measured at regular intervals. The current (and voltage) signals can be received online and contain all the necessary information for the online diagnostics of the EAV condition.
The essence of the approach is to be able to calculate active power signals from the current and voltage signals, and then extract characteristics (‘diagnostic signs’) from certain sections (segments) of the active power signals, according to the values of which the state of the EAV can be diagnosed.
The paper is focused on the problem of automating the division of active power signals into segments (segmentation). In order to transfer the segmentation process to automatic mode, the authors have developed an algorithm based on the use of a convolutional neural network.
The developed convolutional neural network makes it possible to perform online segmentation of active power signals of the EAV. The network has shown good results, which will allow automated monitoring of the technical condition of the EAV when the reactor is operating at full capacity. As a result, the quality of the EAV operation is improved while the failure rate is reduced.
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