Abstract When abnormal events occur in a nuclear power plant, operators must conduct appropriate abnormal operating procedures. It is burdensome though for operators to choose the appropriate procedure considering the numerous main plant parameters and hundreds of alarms that should be judged in a short time. Recently, various research has applied deep-learning algorithms to support this problem by classifying each abnormal condition with high accuracy. Most of these models are trained with simulator data because of a lack of plant data for abnormal states, and as such, developed models may not have tolerance for plant data in actual situations. In this study, two approaches are investigated for a deep-learning model trained with simulator data to overcome the performance degradation caused by noise in actual plant data. First, a preprocessing method using several filters was employed to smooth the test data noise, and second, a data augmentation method was applied to increase the acceptability of the untrained data. Results of this study confirm that the combination of these two approaches can enable high model performance even in the presence of noisy data as in real plants.

中文翻译：

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容忍工厂数据中噪声的异常状态诊断模型

摘要 当核电厂发生异常事件时，操作人员必须执行相应的异常操作程序。然而，考虑到大量的主要工厂参数和应在短时间内判断的数百个警报，操作员选择合适的程序是很麻烦的。最近，各种研究已经应用深度学习算法通过高精度分类每个异常情况来支持这个问题。由于缺乏异常状态的工厂数据，这些模型中的大多数是用模拟器数据训练的，因此，开发的模型可能无法容忍实际情况下的工厂数据。在这项研究中，研究了两种方法用于使用模拟器数据训练的深度学习模型，以克服由实际工厂数据中的噪声引起的性能下降。第一的，使用多个滤波器的预处理方法用于平滑测试数据噪声，其次，应用数据增强方法来增加未训练数据的可接受性。这项研究的结果证实，即使在真实植物中存在噪声数据的情况下，这两种方法的组合也可以实现高模型性能。