Remaining Useful Life Prediction Techniques of Electric Valves for Nuclear Power Plants with Convolution Kernel and LSTM

Autor: Hang Wang, Min-jun Peng, Yong-kuo Liu, Shi-wen Liu, Ren-yi Xu, Hanan Saeed
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Zdroj: Science and Technology of Nuclear Installations, Vol 2020 (2020)
Druh dokumentu: article
ISSN: 1687-6075
1687-6083
DOI: 10.1155/2020/8349349
Popis: Electric valves have significant importance in industrial applications, especially in nuclear power plants. Keeping in view the quantity and criticality of valves in any plant, it is necessary to analyze the degradation of electric valves. However, it is difficult to inspect each valve in conventional maintenance. Keeping in view the quantity and criticality of valves in any plant, it is necessary to analyze the degradation of electric valves. Thus, there exists a genuine demand for remote sensing of a valve condition through nonintrusive methods as well as prediction of its remaining useful life (RUL). In this paper, typical aging modes have been summarized. The data for sensing valve conditions were gathered during aging experiments through acoustic emission sensors. During data processing, convolution kernel integrated with LSTM is utilized for feature extraction. Subsequently, LSTM which has an excellent ability in sequential analysis is used for predicting RUL. Experiments show that the proposed method could predict RUL more accurately compared to other typical machine learning and deep learning methods. This will further enhance maintenance efficiency of any plant.
Databáze: Directory of Open Access Journals
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