Control chart based on likelihood ratio for monitoring linear profiles
Computational Statistics & Data Analysis
Monitoring nonlinear profiles using support vector machines
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
A multivariate synthetic double sampling T2 control chart
Computers and Industrial Engineering
Multivariate adaptive approach for monitoring simple linear profiles
International Journal of Data Analysis Techniques and Strategies
Multivariate process control for detection and cause identification of location shifts
International Journal of Data Analysis Techniques and Strategies
Multivariate adaptive approach for monitoring simple linear profiles
International Journal of Data Analysis Techniques and Strategies
Multivariate process control for detection and cause identification of location shifts
International Journal of Data Analysis Techniques and Strategies
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In some quality control applications, quality of a product or process can be characterized by a relationship between two or more variables that is typically referred to as profile. Moreover, in some situations, there are several correlated quality characteristics, which can be modeled as a set of linear functions of one explanatory variable. We refer to this as multivariate simple linear profiles structure. In this paper, we propose the use of three control chart schemes for Phase II monitoring of multivariate simple linear profiles. The statistical performance of the proposed methods is evaluated in term of average run length criterion and reveals that the control chart schemes are effective in detecting shifts in the process parameters. In addition, the applicability of the proposed methods is illustrated using a real case of calibration application.