S. I. Chang and E. S. HO, “A two-stage neural network approach for process variance change detection and classification,” International Journal of Production Research, Vol. 37, No. 7, pp. 1581–1599, 1999.

has been cited by the following article:

• TITLE: Automated Identification of Basic Control Charts Patterns Using Neural Networks

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

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

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.3 No.3, April 8, 2010

  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.