The recurrent neural network and its variants have shown great success in processing sequences in recent years. However, this deep neural network has not aroused much attention in anomaly detection through predictively process monitoring. Furthermore, the traditional statistic models work on assumptions and hypothesis tests, while neural network (NN) models do not need that many assumptions. This flexibility enables NN models to work efficiently on data with time-varying variability, a common inherent aspect of data in practice. This paper explores the ability of the recurrent neural network structure to monitor processes and proposes a control chart based on long short-term memory (LSTM) prediction intervals for data with time-varying variability. The simulation studies provide empirical evidence that the proposed model outperforms other NN-based predictive monitoring methods for mean shift detection. The proposed method is also applied to time series sensor data, which confirms that the proposed method is an effective technique for detecting abnormalities.

翻译：近年来，循环神经网络及其变体在序列处理方面取得了显著成功。然而，这种深度神经网络在通过预测性过程监测进行异常检测方面并未引起广泛关注。此外，传统统计模型依赖于假设和假设检验，而神经网络模型则无需过多假设。这种灵活性使得神经网络模型能够高效处理具有时变变异性的数据——这是实际数据中常见的固有特征。本文探索了循环神经网络结构在过程监测中的能力，并提出了一种基于长短期记忆预测区间的控制图，用于处理具有时变变异性的数据。仿真研究提供了实证证据，表明所提模型在均值漂移检测方面优于其他基于神经网络的预测性监测方法。所提方法还被应用于时间序列传感器数据，证实了该方法是一种有效的异常检测技术。