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Journal of Applied Sciences
  Year: 2014 | Volume: 14 | Issue: 12 | Page No.: 1244-1251
DOI: 10.3923/jas.2014.1244.1251
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Utilizing of Dissimilarity Scale-based PCA in Multivariate Statistical Process Monitoring Application
Mohd. Yusri Mohd. Yunus and Jie Zhang

A new Multivariate Statistical Process Monitoring (MSPM) framework is proposed, in which the correlation among of the samples are determined by using dissimilarity scale structure. The typical MSPM system adopts linear-based Principal Component Analysis (cPCA) as the multivariate data compression method. Recently however, Classical Scaling-based (CMDS) technique has been proposed as an alternative for reducing the multivariate space, nonetheless, it demands new sets of monitoring schemes as well as statistics. This proposed approach still retains the conventional PCA as the main data compression technique as well as employs the original Hotelling’sT2 and SPE statistics for charting the monitoring status via Shewhart control chart. Therefore, the original conceptual applications of MSPM are greatly preserved to certain extent, without heavily focusing on new terminologies as can be experienced in the previous CMDS systems. There are twenty different cases of Tennessee Eastman Process (TEP) have been chosen for demonstration and the fault detection results of the proposed approach were comparatively analyzed to the outcomes of conventional MSPM based on two performance factors-total number of detected cases and also total number of fastest detection cases. The last two measures are determined through fault detection time. The overall outcomes show that the new technique produces almost comparable performances to the conventional MSPM based monitoring system in terms of number of cases detected, whereas, the City-block scale has been found the most efficient detection scheme among of all. More importantly, these effective monitoring outcomes can be performed based on lower number of PCS models.
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How to cite this article:

Mohd. Yusri Mohd. Yunus and Jie Zhang, 2014. Utilizing of Dissimilarity Scale-based PCA in Multivariate Statistical Process Monitoring Application. Journal of Applied Sciences, 14: 1244-1251.

DOI: 10.3923/jas.2014.1244.1251








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