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A New Machine Learning Based Approach to NASA's Propulsion Engine Diagnostic Benchmark Problem

Abstract Gas turbine engine for aircraft propulsion represents one of the most physics-complex and safety-critical systems in the world. Its failure diagnostic is challenging due to the complexity of the model system, difficulty involved in practical testing and the infeasibility of creating homogeneous diagnostic performance evaluation criteria for the diverse engine makes.

NASA has designed and publicized a standard benchmark problem for propulsion engine gas path diagnostic that enables comparisons among different engine diagnostic approaches. Some traditional model-based approaches and novel purely data-driven approaches such as machine learning, have been applied to this problem.

This study focuses on a different machine learning approach to ... (more)
Created Date 2015
Contributor Wu, Qiyu (Author) / Si, Jennie (Advisor) / Wu, Teresa (Committee member) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Subject Electrical engineering / gas turbine engine / machine learning / support vector machine
Type Masters Thesis
Extent 35 pages
Language English
Reuse Permissions All Rights Reserved
Note Masters Thesis Electrical Engineering 2015
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis