Deep learning methods based on supervised learning have various applications in chemical process fault detection and diagnosis (FDD). However, these methods as black boxes are unable to provide guidance for process recovery, and the labels and fault samples available in actual chemical processes are insufficient for ideal supervised learning. In this study, eMixDTFCN containing FDD backbone network, semi-supervised learning framework, and explainable method is proposed for the first time to address the above challenges simultaneously. Dense temporal feature convolutional network (DTFCN) combining process variable embedding and dense temporal convolution is taken as the backbone network, where the sparse features from the high-dimensional embedded space are learned efficiently through the dense temporal convolution. MixMatch based on consistent regularization and entropy minimization enables DTFCN with semi-supervised learning and imbalanced learning. SHAP method trains an explainer to provide fault-related variables for process recovery. The high effectiveness and comprehensiveness of eMixDTFCN are validated in two typical chemical processes.

中文翻译：

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化工过程综合故障检测与诊断方法


基于监督学习的深度学习方法在化工过程故障检测和诊断（FDD）中具有多种应用。然而，这些黑匣子方法无法为过程恢复提供指导，并且实际化学过程中可用的标签和故障样本不足以实现理想的监督学习。在这项研究中，首次提出了包含FDD骨干网络、半监督学习框架和可解释方法的eMixDTFCN来同时解决上述挑战。结合过程变量嵌入和密集时间卷积的密集时间特征卷积网络（DTFCN）作为主干网络，通过密集时间卷积有效地学习高维嵌入空间中的稀疏特征。基于一致正则化和熵最小化的MixMatch使DTFCN具有半监督学习和不平衡学习的能力。 SHAP 方法训练解释器为过程恢复提供与故障相关的变量。 eMixDTFCN 的高效性和全面性在两种典型的化学工艺中得到了验证。