PREDICTIVE MAINTENANCE USING MACHINE LEARNING AND EXISTING DATA SOURCES
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Authors
Frazier, William J.
Subjects
machine learning
predictive maintenance
conditional probability of failure
naval aviation
predictive maintenance
conditional probability of failure
naval aviation
Advisors
Rowe, Neil C.
Zhao, Ying
Date of Issue
2022-09
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
The United States Marine Corps must address material-readiness challenges with emerging technologies at minimum cost. Predictive maintenance using machine learning is a growing field that can be applied using free or commercial-off-the-shelf software. Naval aviation organizations already maintain a network of data repositories that collect and store current and historical data on repairable flight-critical components. Many components fail before their expected structural life as published their manufacturers, which results in costly unscheduled maintenance. The ability to predict component failures and plan for their replacement or repair can significantly increase operational readiness. This thesis develops and analyzes machine-learning models to predict the conditional probability of failure of various MV-22B flight-critical components using data from existing Naval aviation repositories. Data preprocessing, model training, and predictions use commercial-off-the-shelf software. This work can help improve material readiness and acclimatize military-aviation personnel to emerging technologies in decision making.
Type
Thesis
Description
Includes supplementary material
Series/Report No
Department
Computer Science (CS)
Organization
Identifiers
NPS Report Number
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Citation
Distribution Statement
Approved for public release. Distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.