Integrating Durbin-Watson Statistic and Taguchi Method For Independent Component Selection
| Autor: | Kai-Yi Huang, 黃凱翊 |
|---|---|
| Rok vydání: | 2013 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 101 Independent Component Analysis (ICA) is a multivariate technique aims at linearly transforming correlated variables into independent component. The transforming procedures include whitening, non-Gaussian and component selection. Recently, ICA has been widely used for monitoring non-Gaussian processes. However, the process fault detectability strongly dependents on the selected components. Traditionally, the Euclidean’s L2 norm and Durbin-Watson (DW) statistic were applied for opting the significant IC components. However, the main drawback of both methods still needs engineers’ effort to select the significant IC components. In this study, a simple and objective method will be developed to address the problem. This study proposed to integrate DW statistic and Taguchi method. First of all, the Orthogonal Array (OA) is adopted to experiment several possible combinations of selected components. After that, the DW embedded Signal-to-Noise (SN) ratio is used to measure the experiment results and ultimately in a bid to select significant IC components. The efficiency of the proposed method will be verified via three examples that included a non-Gaussian simulated process and two real case studies that came from Taiwan Power Company and Tennessee Eastman process, respectively. Experiments demonstrated that the proposed method can use lesser ICs to achieve superior performance. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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