The study on fault detection and diagnosis (FDD) of chemical processes has always been the top priority of the chemical process safety. In this paper, a fault diagnosis method combining the deep convolutional with the recurrent neural network (DCRNN) is proposed. In this method, the data from chemical processes are input to the deep convolutional neural network (DCNN) to extract features in spatial domains, and then, the features are fused into the bidirectional recurrent neural network (BRNN). Due to the powerful capabilities of DCNN to extract features in spatial domains and the sensitivity to time series of RNN, the combined method can adaptively learn the dynamic information of the raw data in both spatial and temporal domains and has unique advantages in multivariate chemical processes. The application of the DCRNN model in the Tennessee Eastman (TE) process demonstrates the high accuracy of this proposal in identifying abnormal conditions for the chemical process, compared with the traditional fault identification algorithms of deep learning.

中文翻译：

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基于组合深度学习方法的化工过程故障诊断

化工过程故障检测与诊断（FDD）研究一直是化工过程安全的重中之重。本文提出了一种将深度卷积与循环神经网络（DCRNN）相结合的故障诊断方法。在该方法中，来自化学过程的数据输入到深度卷积神经网络（DCNN）以提取空间域中的特征，然后将这些特征融合到双向循环神经网络（BRNN）中。由于DCNN强大的空间域特征提取能力和RNN对时间序列的敏感性，组合方法可以自适应地学习原始数据在时空域的动态信息，在多元化学过程中具有独特的优势。