PDF(Pubmed)
Abstract：
The reactor coolant pump is a key equipment in a nuclear power plant. If the leakage exceeds a certain threshold, it may cause reactor overheating and shutdown. The reactor coolant pump leakage fault usually has two problems: corrosion and scaling. Accurately and efficiently diagnosing the leakage fault mode as early as possible and predicting its remaining useful life (RUL) are important for taking timely maintenance measures. In this paper, an integrated method is proposed. First, the cross-sectional area of the first seal is extracted as a fault indicator. The motivation is that corrosion may enlarge the cross-sectional area, and scaling may reduce the cross-sectional area. Based on the fluid mechanics theory, an integrated model with several uncertain parameters is established among the cross-sectional area, temperature, and leakage at the inlet and outlet of the first seal. In the diagnosing process, a modified change-detection method is proposed to detect the starting point of degradation. Then, the unknown parameters in the previous relation are estimated, and the degrading data before the starting point of degradation are used to diagnose the leakage fault mode. Second, a time-series model of the autoregressive integrated moving average (ARIMA) is established to predict the remaining useful life based on the degrading data after the starting point of degradation. Finally, the leakage degrading data from six reactor coolant pumps of a nuclear power plant is used to perform the leakage fault mode diagnosis and life prediction with degradation point detection error rates not exceeding 4%, fault mode diagnosis correction rates 100% and practical RUL predicting results, which proves that the proposed integrated method is accurate and efficient. The proposed integrated method combines the advantages of both the physical model diagnosis and the data-driven model diagnosis and innovatively make use of the quantity of flow from the output side of the primary pump as the monitoring indicator and the cross-sectional area as the characteristic index together to diagnose the leakage fault mode happened to the seal and predict its RUL, which can meet the needs of actual operation and maintenance to ensure a healthy and stable operation of the pump and prevent unexpected shutdowns of nuclear power plants and serious accidents.
摘要：
反应堆冷却剂泵是核电站的关键设备。如果泄漏超过某一阈值，它可能导致反应堆过热和停机。反应堆冷却剂泵泄漏故障通常存在两个问题：腐蚀和结垢。尽早准确有效地诊断泄漏故障模式并预测其剩余使用寿命（RUL）对于及时采取维护措施非常重要。在本文中,提出了一种综合方法。首先,提取第一密封件的横截面面积作为故障指示器。动机是腐蚀可能会扩大横截面积，和缩放可以减小横截面积。基于流体力学理论,在横截面积之间建立了具有多个不确定参数的集成模型，温度,以及在第一密封件的入口和出口处的泄漏。在诊断过程中,提出了一种改进的变化检测方法来检测退化的起点。然后，估计先前关系中的未知参数，并且在退化开始点之前的退化数据用于诊断泄漏故障模式。第二,建立了自回归综合移动平均(ARIMA)时间序列模型,根据退化起点后的退化数据预测剩余使用寿命。最后,利用某核电厂六个反应堆冷却剂泵的泄漏退化数据进行泄漏故障模式诊断和寿命预测,退化点检测误差率不超过4%,故障模式诊断修正率100%和实用的RUL预测结果，证明了所提出的集成方法的准确性和高效性。所提出的集成方法结合了物理模型诊断和数据驱动模型诊断的优点，创新性地利用主泵输出侧的流量作为监测指标，横截面积作为特征指标一起诊断密封发生的泄漏故障模式并预测其RUL。能满足实际运行和维护的需要，确保泵的健康稳定运行，防止核电站意外停机和严重事故的发生。
