Improved Predictive Unmanned Aerial Vehicle Maintenance Using Business Analytics and Cloud Services

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Kim, Taejin
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Demand for Unmanned Aerial Vehicle (UAV) usage in various industries is rapidly increasing from one year to another, but at the same time, there is growing concern about the electrical, mechanical, and system reliability of UAVs. The problem is that those reliability issues will interfere with safe operations and may lead to accidents due to malfunctions during flight. One of the effective ways to solve the issues is to strengthen the existing UAV maintenance method. For that reason, this research paper will review data analytics technologies that are using with existing predictive maintenance in aviation or other industries. Furthermore, this paper explains the strengths and weaknesses of each technology and compares each technology of the existing maintenance methods with a Proposed Maintenance Method (PMM) that uses Microsoft Power BI and Azure to verify which maintenance method is better to improve UAV reliability. Lastly, the paper discusses a limitation of the PPM and other efforts to increase the UAV reliability.
preventive maintenance , data analytics technology , UAV reliability , proposed maintenance method (PMM)
Attribution-NonCommercial-NoDerivs 3.0 United States , openAccess