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Information theoretic framework for process control
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DOI：10.1016/S0377-2217(97)00364-0.png)
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摘要
En 中文
This paper proposes a general framework for constructing control charts based on information theory. The potential applications include developing information charts, for monitoring moments and distributions of a process variable and process attributes. In information theoretic process control (ITPC), process moments are mapped to process distributions at in-control and monitoring states, and then to a control function via constrained maximization of entropy and minimization of Kullback-Leibler function (cross-entropy). Variants of information charts can be developed without using distributional assumptions and based on a single criterion function, the information discrepancy between two distributions. An example of an information chart, Information mean-variance chart, IMV-chart, for monitoring process mean and variance is developed. The IMV-chart combines the standard (x) over bar-chart and s(2)-chart, and provides an information theoretic explication of the traditional procedures. Based on a run-length study, it is found that the IMV-chart singly possesses certain advantages over the standard two-chart implementation of (x) over bar-chart and s(2)-chart. Multivariate extension, monitoring counts and proportions, and monitoring distributional changes are briefly discussed. (C) 1998 Elsevier Science B.V. All rights reserved.
Keyword:
information theory
process control
functional optimization
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IF:
6
论文数:
2.2W
被引数:
6.4W
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