Development of dissimilarity-based mspm system
This research is about development of dissimilarity matrix based on Multivariate Statistical Process Monitoring (MSPM) system. MSPM is an observation system to validate whether the process is happening according to its desired target. Nowadays, the chemical process industry is highly based on the no...
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| Format: | Undergraduates Project Papers |
| Language: | English |
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2014
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| Online Access: | http://umpir.ump.edu.my/id/eprint/9242/ http://umpir.ump.edu.my/id/eprint/9242/1/Development%20of%20dissimilarity-based%20mspm%20system.pdf |
| _version_ | 1848818441728819200 |
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| author | Nurul Saidatulhaniza, Zahari |
| author_facet | Nurul Saidatulhaniza, Zahari |
| author_sort | Nurul Saidatulhaniza, Zahari |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | This research is about development of dissimilarity matrix based on Multivariate Statistical Process Monitoring (MSPM) system. MSPM is an observation system to validate whether the process is happening according to its desired target. Nowadays, the chemical process industry is highly based on the non-linear relationships between measured variables. However, the conventional Principal Component Analysis (PCA) which applied based on MSPM system is less effective because it only valid for the linear relationships between measured variables. In order to solve this problem, the technique of dissimilarity matrix is used in multivariate statistical process monitoring as alternative technique which models the non-linear process which simultaneously can improve the process monitoring performance. The procedures in MSPM system consists of two main phases basically for model development and fault detection. This research focused on converting dissimilarity matrix to minor product moment before proceeding to PCA process which runs by using Matlab software. The monitoring performance in both techniques were compared and analysed to achieve the aims of this research. 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, it can be the other alternative method 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 |
| first_indexed | 2025-11-15T01:37:43Z |
| format | Undergraduates Project Papers |
| id | ump-9242 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T01:37:43Z |
| publishDate | 2014 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-92422023-08-03T01:54:20Z http://umpir.ump.edu.my/id/eprint/9242/ Development of dissimilarity-based mspm system Nurul Saidatulhaniza, Zahari QA Mathematics This research is about development of dissimilarity matrix based on Multivariate Statistical Process Monitoring (MSPM) system. MSPM is an observation system to validate whether the process is happening according to its desired target. Nowadays, the chemical process industry is highly based on the non-linear relationships between measured variables. However, the conventional Principal Component Analysis (PCA) which applied based on MSPM system is less effective because it only valid for the linear relationships between measured variables. In order to solve this problem, the technique of dissimilarity matrix is used in multivariate statistical process monitoring as alternative technique which models the non-linear process which simultaneously can improve the process monitoring performance. The procedures in MSPM system consists of two main phases basically for model development and fault detection. This research focused on converting dissimilarity matrix to minor product moment before proceeding to PCA process which runs by using Matlab software. The monitoring performance in both techniques were compared and analysed to achieve the aims of this research. 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, it can be the other alternative method 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 2014-01 Undergraduates Project Papers NonPeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/9242/1/Development%20of%20dissimilarity-based%20mspm%20system.pdf Nurul Saidatulhaniza, Zahari (2014) Development of dissimilarity-based mspm system. Faculty of Chemical & Natural Resources Engineering, Universiti Malaysia Pahang. |
| spellingShingle | QA Mathematics Nurul Saidatulhaniza, Zahari Development of dissimilarity-based mspm system |
| title | Development of dissimilarity-based mspm system |
| title_full | Development of dissimilarity-based mspm system |
| title_fullStr | Development of dissimilarity-based mspm system |
| title_full_unstemmed | Development of dissimilarity-based mspm system |
| title_short | Development of dissimilarity-based mspm system |
| title_sort | development of dissimilarity-based mspm system |
| topic | QA Mathematics |
| url | http://umpir.ump.edu.my/id/eprint/9242/ http://umpir.ump.edu.my/id/eprint/9242/1/Development%20of%20dissimilarity-based%20mspm%20system.pdf |