| 000 | 02384nam a2200265 a 4500 | ||
|---|---|---|---|
| 001 | vtls000075850 | ||
| 003 | KUKTEM | ||
| 005 | 20251114204540.0 | ||
| 008 | 131106t2013 my da f m 000 0 eng d | ||
| 020 | _aTHE0002030(Local) | ||
| 039 | 9 |
_a201905131611 _byusri _c201311121657 _dnabilah _y201311061129 _znabilah |
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| 040 | _aUMP | ||
| 090 | _aQA278.5 .L59 2013 rs Bc. | ||
| 100 | 0 | _aSiti Nur Liyana Ahamd | |
| 245 | 1 | 0 |
_aImplementing PCA-based fault detection system based on selected imported variables for continuous-based process / _cSiti Nur Liyana Ahamd |
| 260 |
_aKuantan, Pahang : _bUMP, _c2013 |
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| 300 |
_axiv, 50 p. : _bill. ; _c30 cm. + _e1 CD-ROM |
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| 502 | _aProject paper (Bachelor of Chemical Engineering) -- Universiti Malaysia Pahang – 2013 | ||
| 504 | _aBibliography : p. 42-44 | ||
| 520 | 3 | _aNowadays, 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. | |
| 650 | 0 | _aPrincipal component analysis | |
| 650 | 0 | _aMultivariate analysis | |
| 650 | 0 |
_aProcess control _xStatistical methods |
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| 999 |
_aVIRTUA40 _c3946 _d3952 |
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