کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
10128881 1645148 2018 12 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Framework for fault diagnosis with multi-source sensor nodes in nuclear power plants based on a Bayesian network
ترجمه فارسی عنوان
چارچوب تشخیص خطا با گره های سنسور چند منبع در نیروگاه های هسته ای بر اساس یک شبکه بیزی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی انرژی مهندسی انرژی و فناوری های برق
چکیده انگلیسی
Fault detection and diagnosis (FDD) provides safety alarms and diagnostic functions for a nuclear power plant (NPP), which comprises large and complex systems. Here, a technical framework based on a Bayesian network (BN) for FDD is introduced because of its advantages of easy visualization, expression of parameter uncertainties, and ability to perform diagnosis with incomplete data. However, a BN raises a new problem when it is applied to NPPs; i.e., how to cope with parameter or node information from multiple sensors. Sensor data must be consolidated because creating a single node for each sensor in the network would lead to information overload. This paper proposes a possible solution to this issue and then constructs an FDD system framework with a BN as the backbone. Within this framework, principal component analysis is used to remove information from malfunctioning sensors, and fuzzy theory and data fusion are combined to further improve data accuracy and combine data from multiple sensors into one node. On this basis, a BN inference junction tree algorithm is used in FDD because it can deal with incomplete data. A BN model for a pressurized water reactor is created to validate the method framework. Simulation experiments indicate the suitability of the proposed method for online FDD in NPPs using multi-sensor information. It is thus concluded that the proposed method is a feasible scheme for the FDD of NPPs.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Annals of Nuclear Energy - Volume 122, December 2018, Pages 297-308
نویسندگان
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