Automated Identification of Basic Control Charts Patterns Using Neural Networks

Ahmed Shaban, Mohammed Shalaby, Ehab Abdelhafiez, Ashraf S. Youssef

Faculty of Engineering, Cairo University, Giza, Egypt.
Faculty of Engineering, Fayoum University, Fayoum, Egypt.

DOI: 10.4236/jsea.2010.33026                

Abstract

The identification of control chart patterns is very important in statistical process control. Control chart patterns are categorized as natural and unnatural. The presence of unnatural patterns means that a process is out of statistical control and there are assignable causes for process variation that should be investigated. This paper proposes an artificial neural network algorithm to identify the three basic control chart patterns; natural, shift, and trend. This identification is in addition to the traditional statistical detection of runs in data, since runs are one of the out of control situations. It is assumed that a process starts as a natural pattern and then may undergo only one out of control pattern at a time. The performance of the proposed algorithm was evaluated by measuring the probability of success in identifying the three basic patterns accurately, and comparing these results with previous research work. The comparison showed that the proposed algorithm realized better identification than others.

Keywords

Artificial Neural Networks (ANN), Control Charts, Control Charts Patterns, Statistical Process Control (SPC), Natural Pattern, Shift Pattern, Trend Pattern

Conflicts of Interest

The authors declare no conflicts of interest.

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