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The Hybrid Principal Component Analysis Based on Wavelets and Moving Median Filter.

Authors :
Hutchison, David
Kanade, Takeo
Kittler, Josef
Kleinberg, Jon M.
Mattern, Friedemann
Mitchell, John C.
Naor, Moni
Nierstrasz, Oscar
Rangan, C. Pandu
Steffen, Bernhard
Sudan, Madhu
Terzopoulos, Demetri
Tygar, Doug
Vardi, Moshe Y.
Weikum, Gerhard
Liu, Derong
Fei, Shumin
Hou, Zengguang
Zhang, Huaguang
Sun, Changyin
Source :
Advances in Neural Networks: ISNN 2007; 2007, p994-1001, 8p
Publication Year :
2007

Abstract

The data obtained from any process may be corrupted with noise and outliers which may lead to false-alarm when applying conventional PCA to process monitoring. To overcome the above mentioned limitations of conventional PCA, an approach is developed by combining the ability of wavelets and moving median filter with PCA. This method utilizes the quality of wavelets and moving median filter to preprocess the data to eliminate noise and outliers. At last, this method is applied to fault detection and has a good effect which proves the method is effective and feasible. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISBNs :
9783540723929
Database :
Complementary Index
Journal :
Advances in Neural Networks: ISNN 2007
Publication Type :
Book
Accession number :
33198880
Full Text :
https://doi.org/10.1007/978-3-540-72393-6_118