Statistical features-ANN recognizer for bivariate process mean shift pattern recognition

Masood, Ibrahim and Hassan, Adnan (2010) Statistical features-ANN recognizer for bivariate process mean shift pattern recognition. In: International Conference on Intelligent and Advanced Systems, 15-17 June 2010, Kuala Lumpur.

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Abstract

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.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:Artificial neural network; bivariate process; statistical features input representation; multivariate quality control; statistical features
Subjects:H Social Sciences > HA Statistics
Divisions:Faculty of Mechanical and Manufacturing Engineering > Department of Manufacturing and Industrial Engineering
ID Code:607
Deposited By:Nurul Elmy Mohd. Yusof
Deposited On:30 Dec 2010 12:13
Last Modified:29 Apr 2011 14:41

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