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

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 convention...

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Published inScience and technology of nuclear installations Vol. 2020; no. 2020; pp. 1 - 13
Main Authors Xu, Ren-yi, Liu, Shi-wen, Liu, Yong-kuo, Peng, Min-jun, Wang, Hang, Saeed, Hanan
Format Journal Article
LanguageEnglish
Published Cairo, Egypt Hindawi Publishing Corporation 2020
Hindawi
Hindawi Limited
Subjects
Acoustic emission
Aging
Algorithms
Artificial intelligence
Big Data
Convolution
Data processing
Datasets
Deep learning
Degradation
Feature extraction
Industrial applications
Industrial plants
Kernels
Life prediction
Machine learning
Maintenance
Neural networks
Nuclear energy
Nuclear power plants
Remote sensing
Sequential analysis
Valves
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Summary: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.
ISSN:1687-6075
1687-6083
DOI:10.1155/2020/8349349