Methods to Reduce Backlogged Maintenance of Los Angeles Class Submarines

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.

Name thesis:degree_name

Engineer

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Mechanical Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2021

Author dc:creator

• Musselwhite, Steven Andrew

Advisor dc:contributor.advisor

• Sapsis, Themistoklis

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright MIT

Licence dc:rights.uri

• http://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/139425

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/139425