Implementing PCA-Based Fault Detection System Based on Selected Imported Variables for Continuous-Based Process
Nowadays, the production based on chemical process was rapidly expanding either domestically or internationally. To produce the maximum amount of consistently high quality products as per requested and specified by the customers, the whole process must be considering included fault detection. This i...
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| Format: | Undergraduates Project Papers |
| Language: | English |
| Published: |
2013
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| Online Access: | http://umpir.ump.edu.my/id/eprint/7213/ http://umpir.ump.edu.my/id/eprint/7213/ http://umpir.ump.edu.my/id/eprint/7213/1/CD7097.pdf |
| Summary: | Nowadays, the production based on chemical process was rapidly expanding either domestically or internationally. To produce the maximum amount of consistently high quality products as per requested and specified by the customers, the whole process must be considering included fault detection. This is to ensure that product quality is achieved and at the same time to ensure that the quality variables are operated under the normal operation. There were several methods that commonly used to detect the fault in process monitoring such as using SPC or MSPC. However because of the MSPC can operated with multivariable continuous processes with collinearities among process variables, this technique was used widely in industry. In MSPC have a few methods that were proposed to improve the fault detection such as PCA, PARAFAC, multidimensional scaling technique, partial least squares, KPCA, NLPCA, MPCA and others. Here, in this thesis was to proposed new technique which was by implementing PCA-based fault detection system based on selected imported variables for continuous-based process. This technique was selected depends on the highest number of magnitude of correlation of variables using Matlab Software. The result in this thesis was the fault can be detected using only selected important variables in the process. |
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