A FAULT DETECTION SYSTEM FOR AN AUTOCORRELATED PROCESS USING SPC/EPC/ANN AND SPC/EPC/SVM SCHEMES

The statistical process control (SPC) chart is effective in detecting process faults. One important assumption for using the traditional SPC charts requires that the plotted observations are independent to each other. However, the assumption of inde- pendent observations is not typically applicable in practice. When the observations are autocorrelated, the false alarms are increased, and these improper signals can result in a misinterpretatio Therefore, the use of engineering process control (EPC) has been proposed to overcome this difficulty. Although EPC is able to compensate for the effects of faults, it decreases the monitoring capability of SPC. This study proposes the combina- tion of SPC, EPC and articial neural network (SPC/EPC/ANN) and SPC, EPC and support vector machine (SPC/EPC/SVM) mechanisms to solve this problem. Using the proposed schemes, this study introduces a useful technique to detect the starting time of process faults based on the execution of the binomial random experiments. The effective- ness and the benecial results of the proposed schemes are demonstrated through the use of series simulations.

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