Adaptive nonparametric control chart for time-varying and multimodal processes

• Published in: Journal of process control Vol. 37; pp. 34 - 45
• Main Authors: Kang, Ji Hoon, Yu, Jaehong, Kim, Seoung Bum
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.01.2016
• Subjects: Clustering; Data mining algorithm; False alarms; Multimodality; Multivariate control chart; Time-varying process; Time-varying process; Data mining algorithm; Clustering; Multivariate control chart; Multimodality; False alarms
• ISSN: 0959-1524; 1873-2771
• DOI: 10.1016/j.jprocont.2015.11.005

•We propose a nonparametric control chart, which is based on a clustering algorithm.•Proposed chart can adaptively monitor the time-varying and multimodal processes.•Proposed chart traces the natural changes over time and keeps the chart up to date.•We examine the stability of proposed chart with a case study from TFT-LCD process. Multivariate statistical process control techniques have been widely used to improve processes by reducing variation and preventing defects. In modern manufacturing, because of the complexity and variability of processes, traditional multivariate control charts such as Hotelling's T2 cannot efficiently handle situations in which the patterns of process observations are nonlinear, multimodal, and time varying. In the present study, we propose a nonparametric control chart, which is capable of adaptively monitoring time-varying and multimodal processes. Experiments with simulated and real process data from a thin film transistor-liquid crystal display (TFT-LCD) demonstrate the effectiveness and accuracy of the proposed method.