Regression control chart for autocorrelated data

With the growing of automation in manufacturing, process quality characteristics are being measured at higher rates and data are more likely to be autocorrelated. The residual charts or control charts with modified control limits for autocorrelated data are widely used approaches for statistical process monitoring in the case of autocorrelated process data. Data sets collected from industrial processes may have both a particular type of trend and autocorrelation among adjacent observation. To the best of our knowledge there are not any schemes that monitor autocorrelated and trending process observations directly to detect the mean shift in the process observations. In this thesis, a new regression control chart which is able to detect the mean shift in a production process is presented. This chart is designed for autocorrelated process observations having a linearly increasing trend. Existing approaches may individually cope with autocorrelated or trending data. The proposed chart requires the identification of trend stationary first order autoregressive (trend AR(1) for short) model as a suitable time series model for process observations. In this thesis an integrated neural network structure, which is composed of appropriate number of linear vector quantization networks, multi layer perceptron networks, and Elman networks, is proposed to recognize the autocorrelated and trending patterns. The neural based system performance is evaluated in terms of the classification rate. After recognizing the trending and autocorrelated data by means of neural networks, proposed modified regression control chart for autocorrelated data is used for different magnitudes of the process mean shift, under the presence of various levels of autocorrelation, to determine whether the trending and autocorrelated process is in-control or not. The performance of proposed chart is evaluated in terms of the accurate signal rate and the average run length.