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Data-driven Sensor Recliabration and Fault Diagnosis in Nuclear Power Plants
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Data-driven Sensor Recliabration and Fault Diagnosis in Nuclear Power Plants. |
| 개인저자 | Yao, Wenqing. |
| 단체저자명 | The Pennsylvania State University. Nuclear Engineering. |
| 발행사항 | [S.l.]: The Pennsylvania State University., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 79 p. |
| 기본자료 저록 | Dissertations Abstracts International 80-12B. Dissertation Abstract International |
| ISBN | 9781392319079 |
| 학위논문주기 | Thesis (Ph.D.)--The Pennsylvania State University, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 80-12, Section: B.
Publisher info.: Dissertation/Thesis. Advisor: Watson, Justin. |
| 요약 | This dissertation explores techniques for online monitoring of nuclear power plants, especially pressurized water reactor (PWR) plants, which must have the capabilities to examine and diagnose the health of instrumentation and component, recalibrate faulty sensor measurements, and send maintenance request to the control room. Such techniques will enhance the functionality and reliability of the control and monitoring system and reduce the instrumentation maintenance labor requirement and cost.Two data-driven methods are introduced for sensor recalibration. The first method is recursive adaptive filtering that estimates the plant state parameters from a set of redundant sensor measurements. It corrects the redundant measurements based on the principle of best linear least-squares estimation and also detects and isolates anomalous measurements by adjusting their weights, in real time, based on a sequential log likelihood ratio test of sensor data. The second method is autoregressive support vector regression that is a virtual sensing technique |
| 일반주제명 | Electrical engineering. Mechanical engineering. Nuclear engineering. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
