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.

中文翻译：

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卷积核和LSTM的核电站电动阀的剩余使用寿命预测技术

电动阀在工业应用中特别是在核电站中具有重要的意义。考虑到任何工厂中阀门的数量和重要性，有必要分析电动阀门的性能下降。但是，在常规维护中难以检查每个阀。考虑到任何工厂中阀门的数量和重要性，有必要分析电动阀门的性能下降。因此，真正需要通过非侵入性方法来遥感阀状态以及预测其剩余使用寿命（RUL）。本文总结了典型的老化模式。在老化实验过程中，通过声发射传感器收集了用于感测阀状态的数据。在数据处理期间，与LSTM集成的卷积核用于特征提取。随后，将在顺序分析中具有出色能力的LSTM用于预测RUL。实验表明，与其他典型的机器学习和深度学习方法相比，该方法可以更准确地预测RUL。这将进一步提高任何工厂的维护效率。