Due to the non-cognition of real-time data, rare loss-based risk warning methods can effectively respond to unexpected emergencies. Machine learning has powerful data processing capabilities and real-time computing functions and thus is suitable for offsetting the shortcomings of traditional risk methods. Risk analysis can be easily employed to perform risk-based data classification for a set of process data. However, the risk analysis process is too complicated to label risk levels for all processes, which is hard to satisfy the requirements of the amount of data for supervised learning. Therefore, the present paper focuses on developing semi-supervised learning methods for the construction of real-time risk-based early warning systems. By using fuzzy HAZOP, we estimate the risk of systems quantitatively based on the process data. With the consideration of scarce labeled data and numerous unlabeled information, we develop the generative adversarial network (GAN)-based semi-supervised learning method to identify the process risk timely. Besides, deep network architecture integrated with the convolutional neural network (CNN) is used for the codification of multi-dimensional process data to enhance the generalization of warning models. Finally, the effectiveness of the proposed method is evaluated through a comparative study with different algorithms on a case of multizone circulating reactor (MZCR).

由于无法识别实时数据，因此基于损失的罕见风险预警方法可以有效应对突发事件。机器学习具有强大的数据处理能力和实时计算功能，因此适合弥补传统风险方法的不足。风险分析可以轻松地用于对一组过程数据执行基于风险的数据分类。但是，风险分析过程太复杂，无法标记所有过程的风险水平，这很难满足监督学习的数据量要求。因此，本文着重于开发用于构建基于风险的实时预警系统的半监督学习方法。通过使用模糊HAZOP，我们根据过程数据定量估计系统的风险。考虑到稀缺的标记数据和大量未标记的信息，我们开发了基于生成对抗网络（GAN）的半监督学习方法来及时识别过程风险。此外，将集成有卷积神经网络（CNN）的深度网络体系结构用于多维过程数据的编码，以增强警告模型的通用性。最后，在多区域循环反应器（MZCR）的情况下，通过与不同算法的比较研究，对所提方法的有效性进行了评估。集成有卷积神经网络（CNN）的深度网络体系结构用于多维过程数据的编码，以增强警告模型的通用性。最后，在多区域循环反应器（MZCR）的情况下，通过与不同算法的比较研究，对所提方法的有效性进行了评估。集成有卷积神经网络（CNN）的深度网络体系结构用于多维过程数据的编码，以增强警告模型的通用性。最后，在多区域循环反应器（MZCR）的情况下，通过与不同算法的比较研究，对所提方法的有效性进行了评估。