Theses and Dissertations

Issuing Body

Mississippi State University

Advisor

Davy M. Belk

Committee Member

Michael A. Hamilton

Committee Member

Raed Jaradat

Committee Member

Adrian Sescu

Date of Degree

8-6-2021

Original embargo terms

Visible to MSU only for 2 years

Document Type

Dissertation - Campus Access Only

Major

Aerospace Engineering

Degree Name

Doctor of Philosophy

College

James Worth Bagley College of Engineering

Department

Department of Aerospace Engineering

Abstract

Integrated Vehicle Health Management (IVHM) systems use models and algorithmic techniques to process Condition-based Data (CBD) to offer prognostic information and actionable imperatives in support of Condition-based Maintenance (CBM) for the system. IVHM technology was first introduced by NASA to gather data, diagnose, detect, and predict faults, and support operational and post-maintenance activities in space vehicles. Eventually, it expanded to other vehicle types such as aircraft, ships, and land vehicles [1]. In recent years, the United States Army has been implementing a policy of CBM to transition from preventive to predictive maintenance [2]. One of the many challenges faced by the Army is the lack of accurate methods to assess ground vehicle reliability using modeling and/or simulation. This study aims at developing a Markov Chain-based algorithm that can detect anomalies and that is capable of accurately predicting the operational states of military ground vehicles. Several different Markov Chain Models (MCMs) have been developed and tested in their ability to predict the next state of a vehicle, given its current state (diagnostics and prognostics), and to examine how well a given model can detect unknown measurements (anomaly detection). A target of 90% Correct Classification (PCC) was established for all the vehicle performance data. The results suggest that it is possible to predict at a high level of accuracy the likely operational states of the military vehicles using MCMs. The anomaly detection test results revealed that MCMs can clearly distinguish a change in the performance data, that does not match the expected performance.

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