Simultaneous monitoring of mean vector and covariance matrix of auto‐correlated multivariate multiple linear profiles

Abstract

The aim of this paper is to develop control charts for a simultaneous monitoring of the mean vector and covariance matrix of multivariate multiple linear regression profiles in phase II, when the independence assumption of the observations within each profile is violated, and there is multivariate autoregressive moving average (MARMA)(1,1) autocorrelation structure within each profile. A transformation method is applied to eliminate the autocorrelation, and six control charts for a simultaneous monitoring of the mean vector and covariance matrix of auto‐correlated multivariate multiple linear regression profiles are proposed. Then, through simulation runs, in terms of the average run length (ARL) criterion, it is shown that the proposed methods have better performance in comparison with the competing methods in detecting shifts in the parameters of regression profiles. Finally, the application of the proposed control charts is shown using real data in a machining process.

Comprehensive Scientometric Analysis and Longitudinal SDG Mapping of Quality and Reliability Engineering International Journal

“…Based on APY, the most recent topic from the cluster is "simultaneous monitoring", with an APY score of 2018.5. Bersimis et al [50] and Mahmoud et al [51] feature as the top-cited articles on process control and profile data in our list, cited 445 and 250 times, respectively, while Rahimi et al [52] article is the most recent on simultaneous monitoring.…”