İnsansız Hava Aracı Teknolojisi ve Operatörlüğü - Makale Koleksiyonu
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Öğe Acoustic-based diagnostics for UAV propeller damage using HNR and Gaussian Naive Bayes(Emerald Publishing Limited, 2024) Cinoğlu, BahadırPurpose – The purpose of this study is to determine propeller damage based on acoustic recordings taken from unmanned aerial vehicle (UAV) propellers operated at different thrust conditions on a test bench. Propeller damage is especially critical for fixed-wing UAVs to sustain a safe flight. The acoustic characteristics of the propeller vary with different propeller damages. Design/methodology/approach – For the research, feature extraction methods and machine learning techniques were used during damage detection from propeller acoustic data. First of all, sound recordings were obtained by operating five different damaged propellers and undamaged propellers under three different thrusts. Afterwards, the harmonic-to-noise ratio (HNR) feature extraction technique was applied to these audio recordings. Finally, model training and validation were performed by applying the Gaussian Naive Bayes machine learning technique to create a diagnostic approach. Findings – A high recall value of 96.19% was obtained in the performance results of the model trained according to damaged and undamaged propeller acoustic data. The precision value was 73.92% as moderate. The overall accuracy value of the model, which can be considered as general performance, was obtained as 81.24%. The F1 score has been found as 83.76% which provides a balanced measure of the model’s precision and recall values. Practical implications – This study include provides solid method to diagnose UAV propeller damage using acoustic data obtain from the microphone and allows identification of differently damaged propellers. Using that, the risk of in-flight failures can be reduced and maintenance costs can be lowered with addressing the occurred problems with UAV propeller before they worsen. Originality/value – This study introduces a novel method to diagnose damaged UAV propellers using the HNR feature extraction technique and Gaussian Naive Bayes classification method. The study is a pioneer in the use of HNR and the Gaussian Naive Bayes and demonstrates its effectiveness in augmenting UAV safety by means of propeller damages. Furthermore, this approach contributes to UAV operational reliability by bridging the acoustic signal processing and machine learning.Öğe A study on different flow channel shapes in a vanadium redox flow battery with serpentine flow field(International Journal of Sustainable Aviation, 2022) Kayali İlker; Elden GülşahThe purpose of this study is to investigate the effects of the different flow channel shapes on battery performance, charge, mass and momentum transports in a vanadium redox flow battery with serpentine flow field during discharge process. In line with this purpose, the four different 2-D models having the four different flow channel shapes are developed. These flow channel shapes are rectangle, square, circle and trapezoid, respectively. The obtained results are shown that the best discharge capacity, electrode and electrolyte potential distributions are achieved in the model with the trapezoid channel shape since this channel shape provides a good electrolyte distribution due to having a deeper channel depth. Furthermore, it is brought out that there is a small difference in the concentration distributions of vanadium species (V2+ and VO2+) for all channel shapes and the near-uniform concentration distributions (V3+ and VO2+) for both porous electrodes are obtained with the model with trapezoid channel shape during discharge process.
