Process monitoring plays an important role in chemical process safety management, and fault diagnosis is a vital step of process monitoring. Among fault diagnosis researches, supervised ones are inappropriate for industrial applications due to the lack of labeled historical data in real situations. Thereby, unsupervised methods which are capable of dealing with unlabeled data should be developed for fault diagnosis. In this work, a new unsupervised data mining method based on deep learning is proposed for isolating different conditions of chemical process, including normal operations and faults, and thus labeled database can be created efficiently for constructing fault diagnosis model. The proposed method mainly consists of three steps: feature extraction by the convolutional stacked autoencoder (SAE), feature visualization by the t-distributed stochastic neighbor embedding (t-SNE) algorithm, and clustering. The benchmark Tennessee Eastman process (TEP) and an industrial hydrocracking instance are utilized to illustrate the effectiveness of the proposed data mining method.

过程监控在化学过程安全管理中起着重要作用，而故障诊断是过程监控的关键步骤。在故障诊断研究中，受监督的研究由于缺乏实际情况下的标记历史数据而不适用于工业应用。因此，应开发能够处理未标记数据的无监督方法以进行故障诊断。在这项工作中，提出了一种基于深度学习的新的无监督数据挖掘方法，用于隔离化学过程的不同条件，包括正常操作和故障，从而可以有效地创建标记数据库，以构建故障诊断模型。所提出的方法主要包括三个步骤：卷积堆叠自动编码器（SAE）的特征提取，通过t分布随机邻居嵌入（t-SNE）算法对特征进行可视化，并进行聚类。基准田纳西州伊士曼过程（TEP）和工业加氢裂化实例被用来说明所提出的数据挖掘方法的有效性。