Using sequence information can improve performances in fault detection for serial (temporal) correlated process data. Classical methods firstly construct extended vectors through concatenating current process data and a certain number of previous process data, and then take dimension reduction methods. However, the simple extension of process data may distort the correlation between variables and largely increase the dimensionality. This paper proposes a novel algorithm, called Dynamic Graph Embedding (DGE), for fault detection. DGE adopts augmented matrices instead of extended vectors to encode sequence information. Furthermore, DGE incorporates both time information and neighborhood information to form similarities of different process data. And then DGE is designed to obtain embedding matrices with Markov chain analysis of the similarities. Extensive experimental results on the Tennessee Eastman (TE) benchmark process show the superiority of DGE in terms of missed detection rate (MDR) and false alarm rate (FAR).

使用序列信息可以提高针对串行（时间）相关过程数据的故障检测性能。经典方法首先通过将当前过程数据和一定数量的先前过程数据进行级联来构造扩展向量，然后采用降维方法。但是，过程数据的简单扩展可能会扭曲变量之间的相关性，并大大增加维数。本文提出了一种新的算法，称为动态图嵌入（DGE），用于故障检测。DGE采用增强矩阵而不是扩展向量来编码序列信息。此外，DGE结合了时间信息和邻域信息，以形成不同过程数据的相似性。然后设计DGE并通过相似性的马尔可夫链分析获得嵌入矩阵。