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Nonparametric monitoring of multivariate data via KNN learning
International Journal of Production Research ( IF 9.2 ) Pub Date : 2020-09-01 , DOI: 10.1080/00207543.2020.1812750
Wendong Li 1 , Chi Zhang 2 , Fugee Tsung 2 , Yajun Mei 3
Affiliation  

Process monitoring of multivariate quality attributes is important in many industrial applications, in which rich historical data are often available thanks to modern sensing technologies. While multivariate statistical process control (SPC) has been receiving increasing attention, existing methods are often inadequate as they are sensitive to the parametric model assumptions of multivariate data. In this paper, we propose a novel, nonparametric k-nearest neighbours empirical cumulative sum (KNN-ECUSUM) control chart that is a machine-learning-based black-box control chart for monitoring multivariate data by utilising extensive historical data under both in-control and out-of-control scenarios. Our proposed method utilises the k-nearest neighbours (KNN) algorithm for dimension reduction to transform multivariate data into univariate data and then applies the CUSUM procedure to monitor the change on the empirical distribution of the transformed univariate data. Extensive simulation studies and a real industrial example based on a disk monitoring system demonstrate the robustness and effectiveness of our proposed method.



中文翻译:

通过 KNN 学习对多元数据进行非参数监控

多变量质量属性的过程监控在许多工业应用中很重要,由于现代传感技术,在这些应用中通常可以获得丰富的历史数据。虽然多元统计过程控制 (SPC) 越来越受到关注,但现有方法往往不够充分,因为它们对多元数据的参数模型假设很敏感。在本文中,我们提出了一种新颖的非参数k最近邻经验累积和 (KNN-ECUSUM) 控制图,它是一种基于机器学习的黑盒控制图,用于通过利用广泛的历史数据来监控多变量数据:控制和失控场景。我们提出的方法利用k-最近邻 (KNN) 算法用于降维将多元数据转换为单变量数据,然后应用 CUSUM 过程来监控转换后的单变量数据的经验分布的变化。广泛的模拟研究和基于磁盘监控系统的真实工业示例证明了我们提出的方法的鲁棒性和有效性。

更新日期:2020-09-01
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