التفاصيل البيبلوغرافية
| العنوان: |
Fault Detection and Isolation of a Pressurized Water Reactor Based on Neural Network and K-Nearest Neighbor |
| المؤلفون: |
Amine Naimi, Jiamei Deng, S. R. Shimjith, A. John Arul |
| المصدر: |
IEEE Access, Vol 10, Pp 17113-17121 (2022) |
| بيانات النشر: |
IEEE, 2022. |
| سنة النشر: |
2022 |
| المجموعة: |
LCC:Electrical engineering. Electronics. Nuclear engineering |
| مصطلحات موضوعية: |
Fault classification, fault detection, K-nearest neighbor (KNN), neural networks (NNs), nuclear power plants (NPPs), pressurized water reactor (PWR), Electrical engineering. Electronics. Nuclear engineering, TK1-9971 |
| الوصف: |
Nuclear power plants (NPPs) are complex dynamic systems with multiple sensors and actuators. The presence of faults in the actuators and sensors can deteriorate the system’s performance and cause serious safety issues. This calls for the development of fault detection and diagnosis systems for detection and isolation of such faults. In this study, fault detection and diagnosis (FDD) based on neural networks (NN) and K-nearest neighbour (KNN) algorithm is applied to a pressurized water reactor (PWR). Fault detection is first determined based on the NN. Second, the KNN algorithm is used to classify the faults. The proposed approach is capable of classifying a variety of actuator faults, sensor faults, and multiple simultaneous actuator and sensor faults. A set of simulation results is provided to demonstrate the accuracy of the FDD method. The classifier performance is further compared with other machine learning techniques. |
| نوع الوثيقة: |
article |
| وصف الملف: |
electronic resource |
| اللغة: |
English |
| تدمد: |
2169-3536 |
| Relation: |
https://ieeexplore.ieee.org/document/9706445/; https://doaj.org/toc/2169-3536 |
| DOI: |
10.1109/ACCESS.2022.3149772 |
| URL الوصول: |
https://doaj.org/article/36b9151b37a34256972649945c00098d |
| رقم الانضمام: |
edsdoj.36b9151b37a34256972649945c00098d |
| قاعدة البيانات: |
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