Date of Award

12-2004

Level of Access

Campus-Only Thesis

Degree Name

Master of Science (MS)

Department

Computer Engineering

Advisor

Mohamad T. Musavi

Second Committee Member

Habtom Ressom

Third Committee Member

Sean Ireland

Abstract

The central idea of Principal Component Analysis (PCA) is to reduce the dimensionality of a data set through a linear transformation. PCA has been commonly used for process monitoring by extracting linear features (principal components) of a normal operation and using the resulting features to detect abnormal process deviations. However, conventional (linear) PCA fails to provide principal components that adequately represent nonlinear relationships in a system. In this thesis, a non-linear PCA (NLPCA) method is developed using artificial neural networks to address the limitations of linear PCA. The performance of PCA and NLPCA is compared in process monitoring applications using synthetic and real-world data.

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