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A New Data-Driven Method for Nonlinear Process Monitoring

机译：非线性过程监控的数据驱动新方法

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In this paper, a new data-driven method called just-in-time learning canonical correlation analysis (JITL-CCA) for tackling nonlinearity in process monitoring is proposed. Canonical correlation analysis (CCA)-based fault detection method has been applied for linear static and dynamic processes. However, CCA has deficiency in coping with nonlinearity existing in real applications, as with other well-established multivariate analysis techniques. This deficiency is illustrated by a numerical example. In recent years, nonlinear analysis tools using kernel principles have been proposed. But the main problem lies in the parameter of kernel function is sensitive and difficult to select. This paper constructs JITL-CCA method to realize on-line learning and monitoring, to build local model and to detect faults with simple parameter setting. Based on T2statistic in the feature space, JITL-CCA is validated by the simulation benchmark of CSTR.
机译：本文提出了一种新的数据驱动方法，称为实时学习规范相关分析（JITL-CCA），用于解决过程监控中的非线性问题。基于规范相关分析（CCA）的故障检测方法已应用于线性静态和动态过程。但是，CCA与其他成熟的多元分析技术一样，在应对实际应用中存在的非线性方面也存在不足。数值示例说明了这种不足。近年来，已经提出了使用核原理的非线性分析工具。但是主要问题在于内核函数的参数敏感且难以选择。本文构建了JITL-CCA方法，实现了在线学习和监控，建立了局部模型，并通过简单的参数设置检测故障。基于特征空间中的T2统计量，通过CSTR的仿真基准对JITL-CCA进行了验证。

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