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Data driven approach for fault detection and identification using competitive learning
|M.S.Q111.H3_4048_r.pdf||Version for non-UH users. Copying/Printing is not permitted||4.64 MB||Adobe PDF||View/Open|
|M.S.Q111.H3_4048_uh.pdf||Version for UH users||4.63 MB||Adobe PDF||View/Open|
|Title:||Data driven approach for fault detection and identification using competitive learning|
|Keywords:||Fault location (Engineering)|
Airplanes -- Monitoring
|Abstract:||Condition Based Maintenance (CBM) is the process of executing repairs or taking corrective action when the objective evidence indicates the need for such actions or in other words when anomalies or faults are detected in a control system. The objective of Fault Detection and Identification (FDI) is to detect, isolate and identify these faults so that the system performance can be improved. When condition based maintenance needs to be performed based on just the data available from a control system then Data Driven approach is utilized. The thesis is focused on the data driven approach for fault detection and would use: (i) Unsupervised Competitive Learning, (ii) Frequency Sensitive Competitive Learning, (iii) Conscience Learning and (iv) Self Organizing Maps for FDI purpose. This approach would provide an effective Data reduction technique for FDI so that instead of using the complete data set available from a control system, pre-processing of the available data would be done using vector quantization and clustering approach. The effectiveness of the developed algorithms is tested using the data available from a Vertical Take off and Landing (VTOL) aircraft model.|
|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|
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