A multi-layer neural network model for detecting changes in the process mean
Computers and Industrial Engineering
The nature of statistical learning theory
The nature of statistical learning theory
Using novelty detection to identify abnormalities caused by mean shifts in bivariate processes
Computers and Industrial Engineering
Artificial neural networks to classify mean shifts from multivariate χ2 chart signals
Computers and Industrial Engineering
Expert Systems with Applications: An International Journal
Computers and Industrial Engineering
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A hybrid learning-based model for on-line detection and analysis of control chart patterns
Computers and Industrial Engineering
Neural networks to identify the out-of-control process variables when a MEWMA chart is employed
ASM '07 The 16th IASTED International Conference on Applied Simulation and Modelling
Support Vector Machines for Pattern Classification
Support Vector Machines for Pattern Classification
Lessons in neural network training: overfitting may be harder than expected
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Using neural networks to detect the bivariate process variance shifts pattern
Computers and Industrial Engineering
Using mutual information for selecting features in supervised neural net learning
IEEE Transactions on Neural Networks
Hi-index | 0.00 |
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.