Support vector machines and wavelet packet analysis for fault detection and identification
| dc.contributor.author | Ortiz, Estefan M. | |
| dc.date.accessioned | 2011-07-21T23:56:56Z | |
| dc.date.available | 2011-07-21T23:56:56Z | |
| dc.date.issued | 2006 | |
| dc.description.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. | |
| dc.description.degree | M.S. | |
| dc.identifier.uri | http://hdl.handle.net/10125/20555 | |
| dc.language | eng | |
| dc.publisher | University of Hawaii at Manoa | |
| dc.relation | Theses for the degree of Master of Science (University of Hawaii at Manoa). Electrical Engineering; no. 4067 | |
| dc.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. | |
| dc.subject | Electric fault location--Data processing | |
| dc.title | Support vector machines and wavelet packet analysis for fault detection and identification | |
| dc.type | Thesis | |
| dc.type.dcmi | Text |
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