Quantitative evaluation of multi-rotor UAV propulsion system reliability
The study investigates the reliability assessment of unmanned aerial vehicle (UAV) propulsion systems using quantitative approaches to predict the failure rates of these crucial components. The research is driven by the need for reliable UAV operations, given the significant consequences of propu...
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Format: | Final Year Project |
Language: | English |
Published: |
Nanyang Technological University
2024
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Online Access: | https://hdl.handle.net/10356/177625 |
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Institution: | Nanyang Technological University |
Language: | English |
Summary: | The study investigates the reliability assessment of unmanned aerial vehicle (UAV)
propulsion systems using quantitative approaches to predict the failure rates of these crucial
components. The research is driven by the need for reliable UAV operations, given the
significant consequences of propulsion system failures on safety and performance. This
project aims to fill the gap in current research, which mainly uses linear models, by
introducing the use of the Weibull distribution, a mixed non-linear Weibull model
(NWMM) with Bayesian estimation, and artificial neural networks (ANNs) to accurately
represent the complex failure patterns observed in UAV systems.
By studying UAV-FD dataset, with a specific emphasis on the electric speed controller
(ESC) data, it became evident that the deterioration patterns of UAV components are not
linear. This facilitates understanding of the effects of defects on motor dependability, while
also laying the groundwork for the creation of predictive models that can provide insights
for maintenance schedules and influence enhancements in UAV system design.
The study employed multiple models to assess UAV-FD and test bench datasets, showing
that the Weibull distribution is capable of describing motor degradation under various fault
conditions. This promotes using non-linear analysis to assess UAV reliability. Future
initiatives include run-to-failure experiments and flight testing will collect failure data to
improve the NWMM and investigate NN models' capacity to represent complex
relationships. |
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