NDLI: Multi-variable process data compression and defect isolation using wavelet PCA and genetic algorithm

Content Provider	Springer Nature Link
Author	Chaouch, Hanen Najeh, Tawfik Nabli, Lotfi
Copyright Year	2016
Abstract	This paper characterizes an approach to data compression and defect isolation for the multi-variable process by introducing the wavelet principal component analysis (PCA) and the genetic algorithms. In the defect analysis process, data compression is a critical phase. In our case of study, this can be achieved through the wavelet PCA. We can, through the use of such a technique, filter the data noise and the measurement errors by combining the PCA and the wavelet analysis. The genetic algorithm optimization feature is then employed in structuring the residues of the defect isolation. We present the principle of the PCA as well as the wavelet compression property. Then, we describe both the defect isolation classical method by structuring the residues and the principle of optimizing a problem by the genetic algorithms. The proposed approach is implemented on a statistic system and the Tennessee Eastman process. In comparison with other previous methods, the obtained results mirror the performance of the approach to defect isolation.
Starting Page	869
Ending Page	878
Page Count	10
File Format	PDF
ISSN	02683768
Journal	The International Journal of Advanced Manufacturing Technology
Volume Number	91
Issue Number	1-4
e-ISSN	14333015
Language	English
Publisher	Springer London
Publisher Date	2016-11-29
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Defect isolation Multivariate process Wavelet PCA Genetic algorithm Optimization Industrial and Production Engineering Media Management Mechanical Engineering Computer-Aided Engineering (CAD, CAE) and Design
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Control and Systems Engineering Mechanical Engineering Computer Science Applications Software

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