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首页> 外文期刊>International Journal of Ventilation >Detecting and diagnosing covariance matrix changes in multistage processes
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Detecting and diagnosing covariance matrix changes in multistage processes

机译：在多阶段过程中检测和诊断协方差矩阵变化

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摘要

Multistage process monitoring and fault identification are currently receiving considerable attention. This article focuses on detecting common faults in a multistage process that affect the process covariance matrix. The process covariance matrix monitoring problem is formulated into a multiple hypotheses testing problem. The proposed method is an exponentially weighted moving average chart built on vectors that are transformed from sample covariance matrices of the collected observations. Extensive simulation analysis shows that, compared to alternative methods for multistage process covariance monitoring and diagnosis, the proposed method is capable of not only detecting variation changes quicker but also identifying faults with higher accuracy.
机译：目前，多阶段过程监控和故障识别受到了广泛的关注。本文重点介绍在多阶段过程中检测会影响过程协方差矩阵的常见故障。过程协方差矩阵监视问题被表述为多重假设检验问题。所提出的方法是基于从收集的观测值的样本协方差矩阵转换的向量的指数加权移动平均图。大量的仿真分析表明，与多阶段过程协方差监测和诊断的替代方法相比，该方法不仅能够更快地检测变化，而且能够以更高的精度识别故障。

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