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)
|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|
|Reuse Permissions||All Rights Reserved|
|Note||Masters Thesis Electrical Engineering 2015|
|Collaborating Institutions||Graduate College / ASU Library|
|Additional Formats||MODS / OAI Dublin Core / RIS|