On line detection of mean and variance shift using neural networks and support vector machine in multivariate processes

The effective recognition of unnatural control chart patterns (CCPs) is one of the most important tools to identify process problems. In multivariate process control, the main problem of multivariate quality control charts is that they can detect an out of control event but do not directly determine which variable or group of variables has caused the out of control signal and how much is the magnitude of out of control. Recently machine learning techniques, such as artificial neural networks (ANNs), have been widely used in the research field of CCP recognition. This study presents a modular model for on-line analysis of out of control signals in multivariate processes. This model consists of two modules. In the first module using a support vector machine (SVM)-classifier, mean shift and variance shift can be recognized. Then in the second module, using two special neural networks for mean and variance, it can be recognized magnitude of shift for each variable simultaneously. Through evaluation and comparison, our research results show that the proposed modular performs substantially better than the traditional corresponding control charts. The main contributions of this work are recognizing the type of unnatural pattern and classifying the magnitude of shift for mean and variance in each variable simultaneously.

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► Recognizing the type of unnatural pattern (mean shift or variance shift) and classifying the magnitude of shift for mean and variance for each variable in the multivariate process simultaneously.
► Presenting a modular model for on-line analysis of out-of-control signals in multivariate processes.
► This research introduces a support vector machine (SVM)-classifier as general proposed system for recognition of mean shift and variance shift.
► This research introduces two special neural networks as special proposed system that can recognize the magnitude of shift for mean and variance for each variable simultaneously.