Please use this identifier to cite or link to this item:
Support vector machines and wavelet packet analysis for fault detection and identification
|M.S.Q111.H3_4067_r.pdf||Version for non-UH users. Copying/Printing is not permitted||2.8 MB||Adobe PDF||View/Open|
|M.S.Q111.H3_4067_uh.pdf||Version for UH users||2.8 MB||Adobe PDF||View/Open|
|Title:||Support vector machines and wavelet packet analysis for fault detection and identification|
|Authors:||Ortiz, Estefan M.|
|Keywords:||Electric fault location -- Data processing|
|Abstract:||This thesis examines a data driven fault detection and identification (FDI) method which uses Support Vector Machines (SVM) and the Wavelet Packet Transform (WPT). The primary focus of this thesis is to present a robust data driven fault diagnosis scheme. The investigated scheme has the capability to detect and identify faulty components of a given system through examination of its output due to a specified input The use of the wavelet packet transform serves to draw out those features of the output response which best characterize each of the fault classes for the various components. Support vector machines are used as the diagnosis phase to detect and isolate faults of a given system.|
|Description:||Thesis (M.S.)--University of Hawaii at Manoa, 2006.|
Includes bibliographical references (leaves 60-62).
ix, 62 leaves, bound ill. 29 cm
|Rights:||All UHM dissertations and theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission from the copyright owner.|
|Appears in Collections:||
M.S. - Electrical Engineering|
Please email firstname.lastname@example.org if you need this content in ADA-compliant format.
Items in ScholarSpace are protected by copyright, with all rights reserved, unless otherwise indicated.