Fault detection and Performance Monitoring of Propellers in Electric UAV

Palanisamy, Rajendra Prasath; Kulkarni, Chetan S.; Corbetta, Matteo; Banerjee, Portia

doi:10.1109/aero53065.2022.9843261

Cited by 4 publications

(4 citation statements)

References 12 publications

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“…Unmanned Aerial Vehicles (UAVs) are frequently used in various fields for purposes such as: surveillance, leisure, transport, environmental monitoring, search and rescue, mapping, research of areas affected by disasters as well as for finding targets and fighting. Accidents related to UAVs have increased in proportion to their number [ [1] , [2] , [3] ].…”

Section: Introductionmentioning

confidence: 99%

“…Investment in UAVs research has considerably increased lately due to the fact that they can replace people in some dangerous situations [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

“…If the edge of the blade deteriorates, the increased aerodynamic resistance of the propeller will increase the energy consumption and reduce the angular velocity. The decrease in angular velocity will affect the traction [ [1] , [2] , [3] , 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Finding fault types of BLDC motors within UAVs using machine learning techniques

Andrioaia,

Gaitan

2024

Heliyon

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Section: Introductionmentioning

confidence: 99%

“…Investment in UAVs research has considerably increased lately due to the fact that they can replace people in some dangerous situations [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Finding fault types of BLDC motors within UAVs using machine learning techniques

Andrioaia,

Gaitan

2024

Heliyon

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“…Ghalamchi et al [14] proposed an estimator for detecting and diagnosing propeller degradation on a multicopter aerial robot. Palanisamy et al [15] implemented an extended Kalman filter-based parameter estimation algorithm to identify changes in the propeller aerodynamic efficiency. The author focused on propeller blade performance and damage detection in electric UAVs.…”

Section: Introductionmentioning

confidence: 99%

Comparison of VTOL UAV Battery Level for Propeller Faulty Classification Model

Mohd Sani,

Mohamad Zin,

Mohd Nor

et al. 2024

JOIV : Int. J. Inform. Visualization

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The degradation of batteries in UAVs may result in various problems, such as connectivity troubles, flight delays, and unexpected accidents. Flight safety and reliability are affected by propeller efficiency and performance. This study explores an acoustic-based method to classify propeller faulty conditions in Vertical Take-Off and Landing Unmanned Aerial Vehicles (VTOL UAV). The main objective is to emphasize the difference between classifier models developed using different battery-level flight data. The sound generated by VTOL UAV provides valuable information about the flight performance, essential for effectively monitoring flying conditions and identifying potential faults. This study uses three classification algorithms-Medium Tree (MT), Linear Support Vector Machine (LSVM), and Linear Discriminant (LD), to classify propeller failures of VTOL UAVs. Datasets are collected from three simulated propeller faulty conditions using a wireless microphone connected to a smartphone in an indoor lab environment with a soundproofing mechanism. The Mel Frequency Cepstral Coefficients technique is implemented in MATLAB (R2020a) to extract valuable features from the recorded sound signals. Extracted features from high and low-battery flights are utilized to develop classification models. Classifiers' performance is analyzed to compare the difference between selected models developed using high and low-battery flight data. The accuracy was measured with other samples to test the robustness of classification models. LSVM and MT classification models developed using high-battery flight data produce better accuracy than low-battery flight data in the training and testing phases. LD classification model developed using high-battery flight data produces better accuracy than low-battery flight data in the testing phase only. These results show that battery degradation can affect the performance of the VTOL UAV faulty classification algorithm.

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