Predictive Maintenance Platform Development for UAVs

This project aims to develop a solution for the predictive maintenance of UAVs which will mitigate the burden and risks associated with the traditional reactive and preventive maintenance. By identifying the critical data related to the failures and monitoring/modeling the behaviors of the components based on data/signals during and after the flight, this research will develop the capability to identify the affected component in advance in a predictive manner. It will not only increase the safety of the UAV operation but also reduce the risks for the organization, and at the same time minimize the costs for the in-house maintenance and system check. This project aims at the analysis and development of AI-based predictive models thanks to the data collected during the use of RPAS, in order to generate a health score of its components and hardware.

Faculty Supervisor:

Yaoyao Fiona Zhao

Student:

Partner:

VOZWIN Inc.

Discipline:

Engineering

Sector:

Manufacturing

University:

McGill University

Program: