Recognition of control chart patterns using multi-resolution wavelets analysis and neural networks

Authors:
Yousef Al-Assaf
Affiliations:
American University of Sharjah, P.O. Box 26666, Sharjah, United Arab Emirates
Venue:
Computers and Industrial Engineering
Year:
2004

Citing 4
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Abstract

Control charts pattern recognition is one of the most important tools in statistical process control to identify process problems. Unnatural patterns exhibited by such charts can be associated with certain assignable causes affecting the process. In this paper, multi-resolution wavelets analysis (MRWA) is used to extract distinct features for unnatural patterns by providing distinct time-frequency coefficients. A reduced set of parameters is derived from these coefficients and used as input to an artificial neural network (ANN) classifier. Results show that the performance of the proposed technique in classifying shift, trend and cyclic patterns is superior to that of ANN classifier, which operated on coded observed data.