基于时序数据挖掘的核电厂故障诊断技术研究

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Study on Fault Diagnosis Technology for Nuclear Power Plants Based on Time Series Data Mining

1. Nuclear Safety and Simulation Technique National Defense Key Subject Laboratory; Harbin Engineering University; Harbin, 150001, China;
2. Nuclear Power Institute of China, Chengdu, 610041, China

摘要: 将时序数据挖掘引入核电厂故障诊断,把核电厂的故障诊断当作序列监督学习问题来对待,并采用滑动窗算法将序列监督学习问题转化为经典的监督学习问题。针对反应堆失水事故(LOCA)进行的仿真实验结果表明,在采用滑动窗算法后,诊断精度有一定的提高,再进一步对滑动窗内的时序数据进行特征提取后,诊断精度有了更大的提高,可以解决经典算法无法解决的问题。
- 核动力装置 /
- 故障诊断 /
- 时序数据挖掘 /
- 滑动窗
Abstract: Time series data mining is applied to the fault diagnosis for nuclear power plants.dow method is used to convert the problem to a standard supervised learning problem.Simulation experiment is carried out by LOCA.The simulation results show that the diagnostic accuracy has certain improvement when the sliding-window method is applied.Furthermore,extracting the feature of the time series data in the sliding-window,the diagnostic accuracy is improved greatly.Some problems which can not be solved by classical algorithm can be diagnosed by time series data mining method.
- Nuclear power plants /
- Fault diagnosis /
- Time series data mining /
- Sliding-window