MACHINE LEARNING TECHNIQUES FOR DEVELOPMENT OF A CONDITION-BASED MAINTENANCE PROGRAM FOR NAVAL PROPULSION PLANTS
Therrio, Eric A.
Fargues, Monique P.
MetadataShow full item record
In this thesis, we investigate a specific type of machine learning (ML) algorithm, specifically a support vector machine (SVM) regressor, as the foundation behind a condition-based maintenance (CBM) program for the major components affecting a naval propulsion system (NPS). This program is designed to specifically monitor the degradation of the ship’s engines, the propeller, and the hull. Simulated data generated in previous work by modeling a combined diesel electric and gas NPS is applied to design the SVM and optimize its hyperparameter values—insensitivity, penalty parameter, and kernel spread. Our results show that an optimally tuned and trained SVM algorithm can make predictions with error rates below 0.5%. Results also show our SVM algorithm outperforms the SVM algorithm discussed in previous work. In this work, we established a good base for developing a CBM program for the U.S. Navy.
Approved for public release. distribution is unlimitedIncludes supplementary material
Showing items related by title, author, creator and subject.
Agrawal, B.N. (2005);This paper presents a review of the spacecraft design program at the Naval Postgraduate School. This program is part of the space systems engineering curriculum. In this curriculum, the students take at least one course ...
Klee, Charles W., Jr. (Boston, Massachusetts; Boston University, 1968-06);Over the past several years the United States Atlantic Fleet Anphib1ous Force has been faced with increasing losses of qualified enlisted men. In 1965 the Atlantic Fleet Amphibious Force had more men leaving the Navy than ...
Shick, BethAnn. (Monterey, California. Naval Postgraduate School, 2007-12);The Joint Strike Fighter (JSF) program is the largest Department of Defense (DoD) military aircraft acquisition program to date. The JSF will serve the Air Force, Navy and Marine Corps, as well as many of our key ...