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A LASSO-Based Diagnostic Framework for Multivariate Statistical Process Control
Technometrics, 2011In monitoring complex systems, apart from quick detection of abnormal changes of system performance and key parameters, accurate fault diagnosis of responsible factors has become increasingly critical in a variety of applications that involve rich process data.
Changliang Zou, Wei Jiang, Fugee Tsung
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Industrial use of multivariate statistical analysis for process monitoring and control
Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301), 2003Multivariate statistical analysis has come of age over the last decade. Dofasco, a leading North American provider of Solutions in Steel/spl trade/ and Tembec, a multinational, integrated forest products company have applied this technology in many industrial applications. The intent of the paper is to give an introduction to the application of on-line
Marc Champagne, Michael Dudzic
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Statistical Process Control of Multivariate Systems with Autocorrelation
2011Current industrial processes are characterized by encompassing a large number of interdependent variables, which very often exhibit autocorrelated behavior, due to the dynamic nature of the phenomena involved, associated with the high sampling rates of modern data acquisition systems.
Tiago J. Rato, Marco S. Reis
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Improvement of causal analysis using multivariate statistical process control
Software Quality Journal, 2008Statistical process control (SPC) is a conventional means of monitoring software processes and detecting related problems, where the causes of detected problems can be identified using causal analysis. Determining the actual causes of reported problems requires significant effort due to the large number of possible causes.
Ching-Pao Chang, Chih-Ping Chu
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Statistical process adjustment of multivariate processes with minimum control efforts
2010 IEEE International Conference on Industrial Engineering and Engineering Management, 2010In controlling a multiple-input-multiple-output (MIMO) process, usually all control variables have to be adjusted at each step, which may incur high adjustment cost. This paper proposes a Lasso adjustment algorithm, which minimizes the number of variables to be adjusted at each step.
Li Wang, Kaibo Wang
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STATISTICAL PROCESS CONTROL OF MULTIVARIATE PROCESSES
IFAC Proceedings Volumes, 1994openaire +1 more source
Knowledge graph-based manufacturing process planning: A state-of-the-art review
Journal of Manufacturing Systems, 2023Shuai Zheng
exaly