基于粗糙集理论和支持向量机算法的核电厂故障诊断方法

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Study on Fault Diagnosis in Nuclear Power Plant Based on Rough Sets and Support Vector Machine

College of Naval Architecture and Power, Naval University of Engineering, Wuhan, 430033, China

摘要: 核电厂故障特征复杂多样,具有不确定性。提出一种基于粗糙集理论和支持向量机(SVM)算法的核电厂故障诊断方法。该方法运用粗糙集理论完成对不确定、不完整数据的约简,然后在此基础上设计SVM多级分类器进行故障诊断。最后,将该方法用于核电厂蒸汽发生器传热管破损、冷端破口、汽相破口、热阱丧失等4种典型故障的诊断。研究表明,该方法能够实现对核电厂故障的快速准确诊断。
- 核电厂 /
- 故障诊断 /
- 粗糙集 /
- 支持向量机
Abstract: The faults of Nuclear Power Plant （NPP） are featured with complication and uncertainty. A NPP fault diagnosis method based on Rough Sets （RS） and Support Vector Machine （SVM） is proposed. Firstly, the uncertain data is reduced based on RS theory. According to the chosen reduction a SVM multi-classifier is designed for fault diagnosis. Finally this method is used to diagnose four typical failures, i.e., steam generator tube rupture accident, cold leg rupture accident, vapour phase rupture accident and loss of heat sink accident. The result shows that this method can diagnose the faults of the NPP rapidly and accurately.
- Nuclear power plant /
- Fault diagnosis /
- Rough set /
- Support vector machine