Remaining Useful Life Prediction Based on Improved Temporal Convolutional Network for Nuclear Power Plant Valves

Abstract

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Proper risk assessment and monitoring of critical component is crucial to the safe operation of Nuclear Power Plants. One of the ways to ensure real-time monitoring is the development of Prognostics and Health Management systems for safety-critical equipment. Recently, the remaining useful life prediction (RUL) has been found to be important in ensuring predictive maintenance and avoiding critical component failure. With the development of artificial intelligent techniques, deep learning algorithms are becoming popular for RUL prediction. Consequently, this paper presents RUL prediction techniques for nuclear plant electric gate valves with a temporal convolution network (TCN). The main advantage of using TCN is its ability to capture and process useful information in short-term sensor measurement changes. Moreover, the efficiency of the proposed TCN is enhanced by incorporating a convolution auto-encoder as a preprocessing layer in its structure, which greatly improved the residual convolution mode. The proposed method is verified on the electric gate valves experimental dataset that represents the real-world operation of the valve, and the result obtained is compared with other conventional data-driven approaches. The evaluation result shows impressive performance of the proposed model in predicting the remaining service life of the gate valves used in the nuclear reactor control system. Moreover, the generalization of the proposed model is evaluated on the turbofan engine benchmark dataset. The evaluation result also shows improved performance in the predicted RUL. Broader application of the proposed TCN is envisaged for critical components in other industries.

Keywords

• remaining useful life prediction
• electric gate valve
• temporal convolutional network
• residual convolution
• nuclear power plant