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Multiple Component Analysis and Its Application in Process Monitoring With Prior Fault Data

机译：多分量分析及其在先验故障数据过程监控中的应用

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摘要

Traditional principal component analysis (PCA) based process monitoring method is unsupervised learning method, which builds a statistical model only based on the normal operating dataset. However, in industrial process database, there are also some faulty operating datasets available which are omitted by PCA but may be helpful for fault detection performance improvement. To better utilize both normal operating data and prior faulty data, a modified PCA method, called multiple component analysis (MCA) is presented for monitoring process faults. MCA statistical modelling involves two kinds of data feature extractions. Firstly, based on normal operating data, MCA applies conventional PCA transformation to obtain the principal component features, which describe normal data distribution directions. Then, for the known fault data, non-local preservation projection technique is used to compute fault discriminant features, which describe the fault data distribution directions. Lastly these features are integrated to construct monitoring statistics for fault detection. Simulations on Tennessee Eastman benchmark process show that the proposed method outperforms traditional PCA method in terms of fault detection performance.
机译：传统的基于主成分分析（PCA）的过程监控方法是无监督学习方法，它仅基于正常操作数据集构建统计模型。但是，在工业过程数据库中，还有一些可用的故障操作数据集，PCA省略了这些数据集，但可能有助于提高故障检测性能。为了更好地利用正常运行数据和先前的故障数据，提出了一种改进的PCA方法，称为多组分分析（MCA），用于监视过程故障。 MCA统计建模涉及两种数据特征提取。首先，MCA基于常规操作数据，应用常规PCA变换以获得描述常规数据分布方向的主成分特征。然后，对于已知的故障数据，使用非局部保留投影技术来计算故障判别特征，这些特征描述了故障数据的分布方向。最后，将这些功能集成在一起以构建用于检测故障的监视统计信息。在田纳西州伊士曼基准测试过程的仿真结果表明，该方法在故障检测性能方面优于传统的PCA方法。

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