A general framework for monitoring complex processes with both in-control and out-of-control information. (July 2015)

• Record Type: Journal Article
• Authors: Zhang, Chi; Tsung, Fugee; Zou, Changliang
• Is Part Of: Computers & industrial engineering. Volume 85(2015)
• Page Start: 157
• Page End: 168
• Publication Date: 2015-07
• Subjects: EWMA -- Model-free procedure -- Statistical process control -- Support vector machine; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2015.03.007
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: A model-free procedure is proposed for monitoring complex processes. Our method makes use of both historical in control and out of control information. A data-mining model (Support Vector Machine) is utilized to build the control chart. The proposed control chart performs robustly, regardless of the data type and underlying distribution. A real-data example is applied to demonstrate how the proposed procedure is used. Abstract: Processes monitoring using multivariate quality variables remains an important and challenging problem in statistical process control (SPC). Although multivariate SPC has been extensively studied in the literature, the challenges associated with designing robust and flexible control schemes have yet to be adequately addressed. This paper develops a general monitoring framework for detecting location shifts in complex processes by employing data mining methods. The historical in-control (IC) and out-of-control (OC) data are combined to set up a support vector machine (SVM) model. The working status of the process is indicated by the probabilistic outputs of the SVM classifier and the multivariate exponentially weighted moving average strategy is applied to construct the control chart. A fast diagnostic procedure can be implemented as soon as the control chart gives an alarm. Our numerical studies show that the proposed control chart is able to deliver satisfactory IC and OC run-length performance regardless of the underlying distributions and data types. An example using real data from an industrial application demonstrates the effectiveness of the proposed method.