Usual multivariate statistical analysis (MSA) techniques are highly sensitive to outliers because they are based on the least squares fitting or the empirical mean and covariance of the data. Data‐driven process monitoring methods based on usual MSA techniques may be unreliable when outliers are present in the training data. To overcome this deficiency, a robust global–local preserving projection (RGLPP) method is proposed for dimension reduction of the high‐dimensional data contaminated by outliers, and then it is applied to the robust monitoring of industrial processes. Despite the presence of outliers, RGLPP yields robust projection directions that can preserve both global and local structures of the high‐dimensional data. Moreover, based on the robust projection directions of RGLPP, a method is developed for identifying outliers in a multivariate data set. An RGLPP‐based robust process monitoring method is also developed to achieve high‐performance monitoring when the training data of industrial processes contain outliers. The effectiveness and advantages of the proposed method are illustrated using an industrial case study.

中文翻译：

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使用强大的全局-局部保护投影监控工业过程

通常的多元统计分析 (MSA) 技术对异常值高度敏感，因为它们基于最小二乘拟合或数据的经验均值和协方差。当训练数据中存在异常值时，基于常规 MSA 技术的数据驱动过程监控方法可能不可靠。为了克服这一缺陷，提出了一种鲁棒的全局-局部保留投影（RGLPP）方法来对被异常值污染的高维数据进行降维，然后将其应用于工业过程的鲁棒监控。尽管存在异常值，但 RGLPP 产生了稳健的投影方向，可以保留高维数据的全局和局部结构。此外，基于 RGLPP 的鲁棒投影方向，开发了一种方法来识别多变量数据集中的异常值。还开发了一种基于 RGLPP 的稳健过程监控方法，以在工业过程的训练数据包含异常值时实现高性能监控。使用工业案例研究说明了所提出方法的有效性和优点。