Abstract

Predictive maintenance, powered by machine learning models, presents an innovative solution to address the limitations of drone battery maintenance. The core aim of this study is to design and implement machine learning models suitable for drone batteries, predict battery health, and prescribe maintenance actions. The ultimate scope is to improve operational reliability, safety, and cost-efficiency for drone applications while advancing sustainability and efficiency within the drone industry. By integrating predictive maintenance, the management of drone batteries can be optimized, leading to more reliable, and cost-effective drone operations, the proposal will seek to identify different factors that impacts the health and functionality of a drone battery, we will be using data from Dubai Police and SPSS will be used to interpret the data and preprocessing it. The study undertook a precise model development and validation, revealing that the Logistic regression algorithm, showed robust predictive abilities with an accuracy of 100%, this model was able to identify critical predictors of battery failure, such as capacity and charge cycles, offering valuable insights for preventive maintenance scheduling, deploying this model has laid the groundwork for evolved predictive maintenance strategies that potentials to develop operational efficiency and reliability in the use of drones by Dubai Police, setting a pattern for future works within the field.

Library of Congress Subject Headings

Drone aircraft--Batteries; Electric batteries--Economic aspects; Electric batteries--Environmental aspects; Deep learning (Machine learning)

Publication Date

Spring 2024

Document Type

Thesis

Student Type

Graduate

Degree Name

Professional Studies (MS)

Department, Program, or Center

Graduate Programs & Research

Advisor

Sanjay Modak

Advisor/Committee Member

Hammou Messatfa

Campus

RIT Dubai

Plan Codes

PROFST-MS

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