Remaining Useful Life Prediction Techniques of Electric Valves for Nuclear Power Plants with Convolution Kernel and LSTM
المؤلفون المشاركون
Wang, Hang
Peng, Min-jun
Liu, Yong-kuo
Liu, Shi-wen
Xu, Ren-yi
Saeed, Hanan
المصدر
Science and Technology of Nuclear Installations
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-13، 13ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-08-28
دولة النشر
مصر
عدد الصفحات
13
الملخص EN
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.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Wang, Hang& Peng, Min-jun& Liu, Yong-kuo& Liu, Shi-wen& Xu, Ren-yi& Saeed, Hanan. 2020. Remaining Useful Life Prediction Techniques of Electric Valves for Nuclear Power Plants with Convolution Kernel and LSTM. Science and Technology of Nuclear Installations،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1209482
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Wang, Hang…[et al.]. Remaining Useful Life Prediction Techniques of Electric Valves for Nuclear Power Plants with Convolution Kernel and LSTM. Science and Technology of Nuclear Installations No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1209482
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Wang, Hang& Peng, Min-jun& Liu, Yong-kuo& Liu, Shi-wen& Xu, Ren-yi& Saeed, Hanan. Remaining Useful Life Prediction Techniques of Electric Valves for Nuclear Power Plants with Convolution Kernel and LSTM. Science and Technology of Nuclear Installations. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1209482
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1209482
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر