Detection of unbalanced blade on UAV by means of audio signal

Rangel-Magdaleno, José; Ureña, J.; Hernández, Álvaro; Perez-Rubio, Carmen

doi:10.1109/ropec.2018.8661459

Cited by 24 publications

(8 citation statements)

References 17 publications

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“…The proposed model achieved a 97% accuracy in detecting the faulty UAV propeller blades. Similar works were performed by Rangel-Magdaleno et al [18] that implemented the discrete wavelet transform (DWT) and Fourier transform techniques, and Pechan and Sescu [19], who only performed the experimental analysis. A fault diagnosis on the UAV sensors was conducted by Guo et al [20] using short-time Fourier transform (STFT) and convolutional neural network (CNN).…”

Section: Related Workmentioning

confidence: 85%

Vibration-Based Fault Detection in Drone Using Artificial Intelligence

Ghazali

Rahiman

2022

IEEE Sensors J.

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Recent years have seen a huge increase in the study of drones. There is a lot of published articles regarding drone, focusing on control optimization, fault detection, safety mechanisms, etc. In fault detection, most studies focused on the effects of faulty propellers and rotors, and there is very limited academic research on drone arms. In this paper, a fault detection based on the vibration of the multirotor arms using artificial intelligence (AI) is proposed. There are some cases in which, due to accident, the arm of the multirotor crack or loosen. This is normally unnoticeable without disassembly, and if not taken care of, it would have likely resulted in a sudden loss of flight stability, which will lead to a crash. Different types of AI methods are incorporated in this study, namely, fuzzy logic, neuro-fuzzy, and neural network (NN). Their results are compared to determine the best method in predicting the safety of the multirotor. Fuzzy logic and neuro-fuzzy methods provided acceptable decision-making, but the performance of the neuro-fuzzy approach depend on the dataset used because overfit model might give incorrect decision-making. This also applies to the NN technique. Because the vibration data are collected in the laboratory environment without consideration of wind effect, this framework is more suitable for early prediction before flying the multirotor in the outdoor environment.

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Section: Related Workmentioning

confidence: 85%

Vibration-Based Fault Detection in Drone Using Artificial Intelligence

Ghazali

Rahiman

2022

IEEE Sensors J.

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“…Shiri et al [30] use Variational Mode Decomposition (VMD) to remove noise from acoustic signals to detect damage in rotating machines. Rangel-Magdaleno et al [31] use Discrete Wavelet Transform (DWT) in their study to decompose sound signals in detecting the unbalanced blade of a UAV. DWT can be used to extract useful information from a signal, as well as for denoising, compression, and feature extraction [32].…”

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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“…The imbalance can result in structural vibration and extra noise generation (Altinors, Yol & Yaman 2021;Semke et al 2021). For drones, many studies have been conducted to detect and control the rotor imbalance effect (Bondyra et al 2017;de Jesus Rangel-Magdaleno et al 2018;Ghalamchi, Jia & Mueller 2019;Iannace, Ciaburro & Trematerra 2019). Despite the (possibly) small amplitudes of these uncertainties and unsteadiness, the noise features will be significantly altered as sound contains only a tiny portion of the fluid energy.…”

Section: Introductionmentioning

confidence: 99%

An investigation of rotor aeroacoustics with unsteady motions and uncertainty factors

et al. 2023

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The aeroacoustic characteristics of flying vehicles with pitch-fixed rotors differ from traditional helicopters with pitch-controlled rotor blades. Accurate predictions of rotor noise are still challenging because many uncertainty factors and unsteadinesses exist. This work investigates the aeroacoustic effects of rotational speed deviation, rotation speed fluctuation, blade vibration and blade geometric asymmetry. The analysis is based on the efficient computation of rotor noise under different working conditions. The mean aerodynamic variables are computed using the blade element moment theory, while small-amplitude fluctuations are introduced to account for the unsteadiness and uncertainty factors. It is shown that periodic rotation speed fluctuations and blade vibrations can produce significant extra tones. By contrast, if the fluctuations and vibrations are random, the noise level in a wide frequency range is increased. The intriguing result reminds us of the need to revisit the rotor broadband noise sources commonly attributed to turbulent flows. The influences are observer angle dependent, and the extra noise production is more significant in the upstream and downstream directions. The asymmetric blade geometry can cause extra tonal noise at the harmonics of the blade shaft frequency. The noise features of dual rotors are also investigated. Usually, the noise is sensitive to the initial phase difference and rotation directions due to the interference effect. However, the noise features are vastly altered if there are slight differences in the rotation speeds. Although the influences of some factors on rotor noise were already known, the present study provides a more comprehensive analysis of the problem. The results also highlight the need to consider these practical factors for accurate noise prediction of multi-rotor flying vehicles.

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Detection of unbalanced blade on UAV by means of audio signal

Cited by 24 publications

References 17 publications

Vibration-Based Fault Detection in Drone Using Artificial Intelligence

Vibration-Based Fault Detection in Drone Using Artificial Intelligence

Comparison of VTOL UAV Battery Level for Propeller Faulty Classification Model

An investigation of rotor aeroacoustics with unsteady motions and uncertainty factors

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