Abstract As fault detection technologies have been developed, process fault diagnosis at early abnormal stage has come to be considered a major problem. In this work, a method to analyze the root cause of faults is developed to provide proper information at the early abnormal stage. First, principal component analysis (PCA) is used for the early detection of the process fault. Then, the contributions, from which the normal portion is removed, are decomposed by singular value decomposition (SVD) method to select the hierarchical sensors. Finally, the multivariate Granger causality (MVGC) method is used to construct the sensor causalities using the hierarchical sensors. The developed methodology is verified using the liquefied natural gas fractionation process model, which embeds a sufficient number of highly correlated sensors. The results are compared with the conventional principal component analysis method and amplification of the residual contribution method to verify the advantages of the proposed method.

中文翻译：

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使用主成分分析和多元格兰杰因果关系在早期异常阶段进行根本因果关系分析

摘要 随着故障检测技术的发展，早期异常阶段的过程故障诊断已成为一个主要问题。在这项工作中，开发了一种分析故障根本原因的方法，以便在早期异常阶段提供正确的信息。首先，主成分分析（PCA）用于过程故障的早期检测。然后，去除正常部分的贡献通过奇异值分解（SVD）方法分解以选择分层传感器。最后，使用多变量格兰杰因果关系（MVGC）方法来构建使用分层传感器的传感器因果关系。使用液化天然气分馏过程模型验证开发的方法，该模型嵌入了足够数量的高度相关的传感器。