Enhancement of PCA-based fault detection system through utilising dissimilarity matrix for continuous-based process
This research is about enhancement of PCA-based fault detection system through utilizing dissimilarity matrix. Nowadays, the chemical process industry is highly based on the non-linear relationships between measured variables. However, the conventional PCA-based MSPC is no longer effective because i...
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| Format: | Undergraduates Project Papers |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/7160/1/Enhancement_of_PCA-based_fault_detection_system_through_utilising_dissimilarity_matrix.pdf http://umpir.ump.edu.my/id/eprint/7160/ |
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| Summary: | This research is about enhancement of PCA-based fault detection system through utilizing dissimilarity matrix. Nowadays, the chemical process industry is highly based on the non-linear relationships between measured variables. However, the conventional PCA-based MSPC is no longer effective because it only valid for the linear relationships between measured variables. Due in order to solve this problem, the technique of dissimilarity matrix is used in multivariate statistical process control as alternative technique which models the non-linear process and can improve the process monitoring performance. The conventional PCA system was run and the dissimilarity system was developed and lastly the monitoring performance in each technique were compared and analysed to achieve aims of this research. This research is to be done by using Matlab software. The findings of this study are illustrated in the form of Hotelling’s T2 and Squared Prediction Errors (SPE) monitoring statistics to be analysed. As a conclusion, the dissimilarity system is comparable to the conventional method. Thus can be the other alternative ways in the process monitoring performance. Finally, it is recommended to use data from other chemical processing systems for more concrete justification of the new technique. |
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