Representation extraction is crucial in data‐driven process monitoring, and deep neural network (DNN) is an efficient tool for extracting representations from considerable process data. This study proposes a hierarchical representation learning (HRL) method that integrates the deep belief neural (DBN) network and support vector data description (SVDD) for efficient nonlinear chemical process fault diagnosis. First, hierarchical representations containing meaningful process information are generated through a DBN network by utilizing generally massive normal operating process data. Second, an SVDD‐based decision‐making system is constructed using generally small‐sized faulty data. Three experimental studies are then conducted. A comparison of results with those of several state‐of‐the‐art methods reveal the suitability of the HRL method for process monitoring due to its two main advantages. First, DNN has a superior representative ability and generates representations with richer process information than conventional data‐driven methods. Second, the HRL method utilizes available process data and is suitable for practical conditions in which considerable normal operating data but limited small‐sized faulty data are available.

中文翻译：

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基于层次表示学习的数据驱动非线性化学过程故障诊断

表示提取在数据驱动的过程监视中至关重要，而深度神经网络（DNN）是从大量过程数据中提取表示的有效工具。这项研究提出了一种分层表示学习（HRL）方法，该方法将深度置信神经（DBN）网络与支持向量数据描述（SVDD）集成在一起，以进行有效的非线性化学过程故障诊断。首先，通过利用通常大量的正常操作过程数据，通过DBN网络生成包含有意义的过程信息的分层表示。其次，使用一般小的故障数据构造基于SVDD的决策系统。然后进行了三个实验研究。将结果与几种最新方法的结果进行比较，发现HRL方法具有两个主要优点，因此适用于过程监控。首先，与传统的数据驱动方法相比，DNN具有出色的代表能力，并生成具有更丰富过程信息的表示形式。其次，HRL方法利用可用的过程数据，适用于实际条件，在这些条件下，可获得大量正常运行数据，但有少量小型故障数据。