2026-09-20T17:52:28Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1394252022-01-15T03:28:17Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Sapsis, Themistoklis
Musselwhite, Steven Andrew
Massachusetts Institute of Technology. Department of Mechanical Engineering
2022-01-14T15:10:43Z
2022-01-14T15:10:43Z
2021-06
2021-06-29T19:28:17.350Z
https://hdl.handle.net/1721.1/139425
The United States Navy’s submarine fleet operates independently in high-risk situations around the globe. These missions are of vital importance to the nation’s national security, requiring the vessels to maintain very high standards of material condition and readiness. However, increased operational needs, personnel shortages in the civilian workforce, and other factors have resulted in a significant backlog in submarine maintenance. Submarines are governed by stricter standards than other naval assets, preventing them from deploying until required preventive maintenance items and inspections have been completed. This thesis investigates historical performance data to build predictive models for component failures that could be used to shift periodicities for preventive items and reduce the existing backlog.
Test components from the Los Angeles class of attack submarines were chosen for this investigation. Non-parametric and parametric models are fitted to these components, providing quantitative methods to manage the risks associated with periodicity shifts. This process can identify components that consistently fail within the existing periodicity as well as those that have successfully operated beyond that point due to previous deferrals. This presents an opportunity to improve the efficiency of submarine maintenance, although the quality of the Navy’s records was identified as a potential limiting factor.
Nav.E.
S.M.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
Copyright MIT
http://rightsstatements.org/page/InC-EDU/1.0/
Methods to Reduce Backlogged Maintenance of Los Angeles Class Submarines
Thesis
application/pdf
Engineer
Master
Naval Engineer
Master of Science in Mechanical Engineering
Publication
unknown
unknown
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<Abstract>The United States Navy’s submarine fleet operates independently in high-risk situations around the globe. These missions are of vital importance to the nation’s national security, requiring the vessels to maintain very high standards of material condition and readiness. However, increased operational needs, personnel shortages in the civilian workforce, and other factors have resulted in a significant backlog in submarine maintenance. Submarines are governed by stricter standards than other naval assets, preventing them from deploying until required preventive maintenance items and inspections have been completed. This thesis investigates historical performance data to build predictive models for component failures that could be used to shift periodicities for preventive items and reduce the existing backlog.
Test components from the Los Angeles class of attack submarines were chosen for this investigation. Non-parametric and parametric models are fitted to these components, providing quantitative methods to manage the risks associated with periodicity shifts. This process can identify components that consistently fail within the existing periodicity as well as those that have successfully operated beyond that point due to previous deferrals. This presents an opportunity to improve the efficiency of submarine maintenance, although the quality of the Navy’s records was identified as a potential limiting factor.</Abstract>
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