Nonparametric monitoring of multivariate data via KNN learning

• Published in: International journal of production research Vol. 59; no. 20; pp. 6311 - 6326
• Main Authors: Li, Wendong, Zhang, Chi, Tsung, Fugee, Mei, Yajun
• Format: Journal Article
• Language: English
• Published: London Taylor & Francis 18.10.2021; Taylor & Francis LLC
• Subjects: categorical variable; Control charts; CUSUM; empirical probability mass function; Industrial applications; K-nearest neighbors algorithm; KNN algorithm; Machine learning; Monitoring; Multivariate analysis; Multivariate statistical process control; Nonparametric statistics; Process controls; Quality management; Statistical process control
• ISSN: 0020-7543; 1366-588X
• DOI: 10.1080/00207543.2020.1812750

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.