A Denoising Based Autoassociative Model for Robust Sensor Monitoring in Nuclear Power Plants
المؤلفون المشاركون
Yin, Xu-Cheng
Hao, Hong-Wei
Shaheryar, Ahmad
Ali, Hazrat
Iqbal, Khalid
المصدر
Science and Technology of Nuclear Installations
العدد
المجلد 2016، العدد 2016 (31 ديسمبر/كانون الأول 2016)، ص ص. 1-17، 17ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2016-03-22
دولة النشر
مصر
عدد الصفحات
17
الملخص EN
Sensors health monitoring is essentially important for reliable functioning of safety-critical chemical and nuclear power plants.
Autoassociative neural network (AANN) based empirical sensor models have widely been reported for sensor calibration monitoring.
However, such ill-posed data driven models may result in poor generalization and robustness.
To address above-mentioned issues, several regularization heuristics such as training with jitter, weight decay, and cross-validation are suggested in literature.
Apart from these regularization heuristics, traditional error gradient based supervised learning algorithms for multilayered AANN models are highly susceptible of being trapped in local optimum.
In order to address poor regularization and robust learning issues, here, we propose a denoised autoassociative sensor model (DAASM) based on deep learning framework.
Proposed DAASM model comprises multiple hidden layers which are pretrained greedily in an unsupervised fashion under denoising autoencoder architecture.
In order to improve robustness, dropout heuristic and domain specific data corruption processes are exercised during unsupervised pretraining phase.
The proposed sensor model is trained and tested on sensor data from a PWR type nuclear power plant.
Accuracy, autosensitivity, spillover, and sequential probability ratio test (SPRT) based fault detectability metrics are used for performance assessment and comparison with extensively reported five-layer AANN model by Kramer.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Shaheryar, Ahmad& Yin, Xu-Cheng& Hao, Hong-Wei& Ali, Hazrat& Iqbal, Khalid. 2016. A Denoising Based Autoassociative Model for Robust Sensor Monitoring in Nuclear Power Plants. Science and Technology of Nuclear Installations،Vol. 2016, no. 2016, pp.1-17.
https://search.emarefa.net/detail/BIM-1118644
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Shaheryar, Ahmad…[et al.]. A Denoising Based Autoassociative Model for Robust Sensor Monitoring in Nuclear Power Plants. Science and Technology of Nuclear Installations No. 2016 (2016), pp.1-17.
https://search.emarefa.net/detail/BIM-1118644
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Shaheryar, Ahmad& Yin, Xu-Cheng& Hao, Hong-Wei& Ali, Hazrat& Iqbal, Khalid. A Denoising Based Autoassociative Model for Robust Sensor Monitoring in Nuclear Power Plants. Science and Technology of Nuclear Installations. 2016. Vol. 2016, no. 2016, pp.1-17.
https://search.emarefa.net/detail/BIM-1118644
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1118644
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