Identifying the Source of Multivariate Autocorrelated Process Shiftsby Artificial Neural Networks and Support Vector Machine
| Autor: | Yu-Lin Chiu, 邱玉琳 |
|---|---|
| Rok vydání: | 2007 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 95 In many industrial processes, a product may have two or more related quality characteristics which should be monitored simultaneously. However, the measurement data from many manufacturing processes are not independent in practice. Thus, the traditional T-square chart was insufficient for detecting mean shifts in multivariate auto-correlated processes. The Hotelling’s T-square control chart has been designed for detecting mean shifts. In this research, we purposed two mean shifts classifiers based on artificial neural network (ANN) and support vector machine (SVM). When an out-of-control signal is appeared, the classifier will determine which variable is responsible for the mean shifts. In this research, we considered three models of multivariate auto-correlated process. Various shift scenarios expressed in covariance matrices and autocorrelation parameter matrices were investigated. Statistical distance was proposed to be used as the component of the input vectors. The performance of the proposed method was evaluated by computing correct classification accuracy. The results showed that the proposed approach is a successful method in identifying the source of mean change in multivariate auto-correlated process. Results from our experiment also indicated that SVM-based classifier performs better than the neural network-based classifier. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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