Journal of Quality Engineering and Management

A neuro-fuzzy adaptive inference system for statistical control of autocorrelated data processes

Document Type : Original Article

Authors

Mohammad Reza Vakili 1
Abbas Saghai 2
Amin Mahmodi 3

1 Islamic Azad University, Ardabil Branch, Department of Industrial Engineering, Ardabil, Iran.

2 Islamic Azad University, Tehran Science and Research Branch, Industrial Engineering Department, Tehran, Iran.

3 Buin Zahra Technical and Engineering Higher Education Center, Industrial Engineering Department, Qazvin, Iran.

Abstract
Traditional control charts are based on the basic assumption that process data are sequentially independent of each other and have a normal distribution. However, in many real-world cases, including chemical and continuous processes, this basic assumption does not exist and there is a kind of autocorrelation between the data collected from the process. The use of traditional control charts in autocorrelated processes is unreliable and increases false alarms. One of the methods developed to control autocorrelated processes is to identify the structure of the process time series and use the residual values ​​to control the process. In this paper, a model based on neuro-fuzzy adaptive systems is designed to identify the structure of the time series and use the prediction. Finally, it is AR(2). Also, residual control charts based on this system for second-order autoregressive data using simulated data, the efficiency of the proposed method in the weighted moving average chart and for different degrees of correlation are evaluated, and it is shown that the proposed method has very good efficiency for data with high correlation.

Keywords

Statistical process control
Control charts
Adaptive neuro-fuzzy inference system
Autocorrelated data
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[3] Runger . GC, Willemain. TR. (1995), Modelbased and model-free control of autocorrelated processes. J Qual Technol 27(4), pp.283–292.
[4] Harris. T.J, Ross. W.H. (1991), Statistical process control procedures for autocorrelated observations, The Canadian Journal of Chemical Engineering, 69, pp.48–57.
Journal of Quality Engineering and Management
Volume 4, Issue 1
Summer 2014
Pages 33-42

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