Prediction of peak values in time series data for prognostics of critical components in nuclear power plants

Jie Liu; Enrico Zio

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Prediction of peak values in time series data for prognostics of critical components in nuclear power plants

机译：预测时间序列数据中的峰值，以预测核电站的关键组件

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Abstract: Equipment failure prognostics aims at the prediction of the equipment future health condition. For this, a model is trained to fit the monitored data of the condition of the equipment. This can be quite difficult and complicated with large and nonmonotonic data. Peaks in nonmonotonic data are data values which are much larger than the others. For nonmonotonic degradation processes, it can be more efficient to predict the time and value of the peaks during the evolution of the condition of the equipment. In this paper, we propose a framework to predict the values of the future peaks. More specifically, data-driven models are trained to predict the next peak values, based on the information of the peaks in the historical data. The trained models are, then, used to predict the next peak values. A real case study regarding the leakage of the first seal of a reactor coolant pump in a nuclear power plant is considered to verify the effectiveness of the proposed prediction framework.

机译：摘要：设备故障的预测旨在预测设备未来的健康状况。为此，训练模型以适合设备状态的监视数据。对于大型且非单调的数据，这可能非常困难且复杂。非单调数据中的峰是比其他数据大得多的数据值。对于非单调降解过程，在设备状态演变过程中预测峰的时间和峰值可能更有效。在本文中，我们提出了一个预测未来峰值的框架。更具体地说，基于历史数据中峰的信息，训练数据驱动模型以预测下一个峰。然后，将训练后的模型用于预测下一个峰值。考虑了有关核电厂反应堆冷却剂泵的第一密封件泄漏的真实案例研究，以验证所提出的预测框架的有效性。

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《IFAC PapersOnLine》 |2016年第28期|共5页
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Jie Liu; Enrico Zio;
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中图分类自动化技术、计算机技术;
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4. Prediction of peak values in time series data for prognostics of critical components in nuclear power plants [C] . Jie Liu, Enrico Zio IFAC Workshop on Advanced Maintenance Engineering, Services and Technology . 2017

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Prediction of peak values in time series data for prognostics of critical components in nuclear power plants

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