Statistical features-ANN recognizer for bivariate process mean shift pattern recognition
Artificial neural network (ANN)-based recognizers have been developed for monitoring and diagnosis bivariate process mean shift in multivariate statistical process control (MSPC). They have better average run lengths (ARLs) performance in monitoring process mean shifts and gave an useful diagnosis i...
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| Main Authors: | , |
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| 格式: | Conference or Workshop Item |
| 出版: |
2010
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| 主题: | |
| 在线阅读: | http://eprints.uthm.edu.my/607/ http://eprints.uthm.edu.my/607/1/Ibrahim_Masood_(icias2010).pdf |
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| 总结: | Artificial neural network (ANN)-based recognizers have been developed for monitoring and diagnosis bivariate process mean shift in multivariate statistical process control (MSPC). They have better average run lengths (ARLs) performance in monitoring process mean shifts and gave an
useful diagnosis information compared to the traditional MSPC schemes such as T2, multivariate cumulative sum (MCUSUM) and multivariate exponentially weighted moving average (MEWMA). The existing recognizers are raw databased,
whereby raw data input representation were applied into ANN. This approach required in a large network size, more computational effort and training time consuming. In this paper, the statistical features input representation was
investigated, whereby the raw data were transformed into
exponentially weighted moving average, multiplication of mean with standard deviation and multiplication of mean with meansquare value. The statistical features-ANN recognizer resulted in smaller network size, fast training time, better ARLs for monitoring process mean shifts and comparable recognition accuracy for diagnosing the source variable(s) compared to the raw data-ANN recognizer. |
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