Residual-based maximum MCUSUM control chart for joint monitoring the mean and variability of multivariate autocorrelated processes. Issue 1 (1st January 2019)

• Record Type: Journal Article
• Authors: Khusna, Hidayatul; Mashuri, Muhammad; Suhartono, ; Prastyo, Dedy Dwi; Lee, Muhammad Hisyam; Ahsan, Muhammad
• Is Part Of: Production & manufacturing research. Volume 7:Issue 1(2019)
• Page Start: 364
• Page End: 394
• Publication Date: 2019-01-01
• Subjects: Autocorrelated -- average run length (ARL) -- bootstrap -- multioutput least square SVR (MLS-SVR) -- maximum MCUSUM (Max-MCUSUM) control chart; Manufacturing processes -- Periodicals; Production engineering -- Research -- Periodicals; Manufacturing processes; Production engineering -- Research; Periodicals; 670
• DOI: 10.1080/21693277.2019.1622471
• Languages: English
• ISSNs: 2169-3277

Abstract

ABSTRACT: Maximum multivariate cumulative sum (Max-MCUSUM) is one of the single control charts proposed for joint monitoring the mean and variability of independent observation. Since many applications yield time series data, it is important to develop Max-MCUSUM control chart for monitoring multivariate autocorrelated processes. In this paper, we propose a Max-MCUSUM control chart based on the residual of multioutput least square support vector regression (MLS-SVR). The optimal parameters of MLS-SVR model are calculated using historical in-control data and the control limit of the proposed chart is estimated using the bootstrap approach. The average run lengths of MLS-SVR-based Max-MCUSUM chart verify that the proposed chart is more sensitive to detect mean vector shift than to detect a covariance matrix shift. The illustrative examples of the proposed control chart are also provided for both simulation and real data.