Abstract
In practice, many process monitoring and control scenarios involve several related variables. However one of the major problems that arise in using a multivariate control chart is the interpretation of out-of-control signals. Although RAM method is a popular approach for interpreting multivariate control chart signals, the accuracy of this method decreases as the number of out-of-control variables increases. In this paper, we proposed a new approach for multivariate control chart interpretation based on the idea of integrating neural network technology and RAM method. In many multivariate control scenarios, simulation results show that the proposed approach out-performs RAM method.
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Shao, Y.E., Wu, CH., Ho, BY., Hsu, BS. (2008). A Neural Network-Based Approach to Identifying Out-of-Control Variables for Multivariate Control Charts. In: Shen, W., Yong, J., Yang, Y., Barthès, JP.A., Luo, J. (eds) Computer Supported Cooperative Work in Design IV. CSCWD 2007. Lecture Notes in Computer Science, vol 5236. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92719-8_58
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DOI: https://doi.org/10.1007/978-3-540-92719-8_58
Publisher Name: Springer, Berlin, Heidelberg
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