Machine Learning Techniques for Development of a Condition-Based Maintenance Program for Naval Propulsion Plants
Naval Postgraduate School Monterey United States
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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 ships 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 valuesinsensitivity, 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.
- Logistics, Military Facilities and Supplies