A sound based method for fault detection with statistical feature extraction in UAV motors

Altinörs, Ayhan; Yol, Ferhat; Yaman, Orhan

doi:10.1016/j.apacoust.2021.108325

Cited by 70 publications

(31 citation statements)

References 29 publications

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“…The methods based on the analysis of sound signals are proposed by Altinos et al in [117]. Based on information from the microphone, they detect damage in BLDC engines, which are typical solutions for both military and civilian drones.…”

Section: Data-based Methodsmentioning

confidence: 99%

UAV Fault Detection Methods, State-of-the-Art

Puchalski

Giernacki

2022

Drones

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The continual expansion of the range of applications for unmanned aerial vehicles (UAVs) is resulting in the development of more and more sophisticated systems. The greater the complexity of the UAV, the greater the likelihood that a component will fail. Due to the fact that drones often operate in close proximity to humans, the reliability of flying robots, which directly affects the level of safety, is becoming more important. This review article presents recent research works on fault detection on unmanned flying systems. They include papers published between January 2016 and August 2022. Web of Science and Google Scholar databases were used to search for articles. Terminology related to fault detection of unmanned aerial vehicles was used as keywords. The articles were analyzed, each paper was briefly summarized and the most important details concerning each of the described articles were summarized in the table.

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Section: Data-based Methodsmentioning

confidence: 99%

UAV Fault Detection Methods, State-of-the-Art

Puchalski

Giernacki

2022

Drones

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“…A few works describe fault detection in UAVs by analyzing sound [57] [58]. The work in [59] used sound as a source of input data to a Feed Forward Neural Network model that outputs the probability that one of the blades is partially or fully broken.…”

Section: Related Workmentioning

confidence: 99%

A Stethoscope for Drones: Transformers-Based Methods for UAVs Acoustic Anomaly Detection

et al. 2023

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Unmanned Aerial Vehicles and the increasing variety of their applications are raising in popularity. The growing number of UAVs, emphasizes the significance of drones' reliability and robustness. Thus, there is a need for an efficient self-observing sensing mechanism to detect real-time anomalies in drone behavior. Previous works suggested prediction models from control theory, yet, they are complex by nature and hard to implement, while Deep Learning solutions are of great utility. In this paper, we propose a real-time framework to detect anomalies in drones by analyzing the sound emitted from them. For this purpose, we construct a hybrid Deep Learning based Transformer and a Convolutional Neural Network inspired by the well-known VGG architecture. Our approach is examined over a dataset that is collected from a single microphone set located on a micro drone in real-time. Our approach achieves an F1score of 88.4% in detecting anomalies and outperforms the VGG-16 architecture. Moreover, the framework presented in this paper reduces the number of parameters of the well-known VGG-16 from 138M, into a shrunk version with 3.6M parameters only. Additionally, our real-time approach, results in a smaller number of parameters in the neural network, and yet yields high accuracy in anomaly detection in drones with an average inference time of 0.2 seconds per second. Moreover, with an earphone that weighs less than 100 grams on top of the UAV, our method is shown to be beneficial, even in extreme conditions such as a microsize dataset that is composed of three hours of flight recordings. The presented self-observing method can be implemented by simply adding a microphone to drones and transmitting the captured audio for analysis to the remote control or performing it onboard the drone using a dedicated microcontroller.

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“…Altinors et al [ 22 ] diagnosed the fault in the brushless DC (BLDC) motors used in Unmanned Aerial Vehicles (UAV), by utilizing the sound data received from the motors, the

Nearest Neighbor (

-NN), Decision tree (DT), Support Vector Machines (SVM) methods are used for the construction of the fault diagnosis models. Jiang et al [ 23 ] proposed a strong robustness diagnosis strategy based on d-q-axis current signal to diagnose the Open-Circuit (OC) fault in the novel fault-tolerant electric drive system.…”

Section: Introductionmentioning

confidence: 99%

A Novel Supervised Filter Feature Selection Method Based on Gaussian Probability Density for Fault Diagnosis of Permanent Magnet DC Motors

Wang

2022

Sensors

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For permanent magnet DC motors (PMDCMs), the amplitude of the current signals gradually decreases after the motor starts. In this work, the time domain features and time-frequency-domain features extracted from several successive segments of current signals make up a feature vector, which is adopted for fault diagnosis of PMDCMs. Many redundant features will lead to a decrease in diagnosis efficiency and increase the computation cost, so it is necessary to eliminate redundant features and features that have negative effects. This paper presents a novel supervised filter feature selection method for reducing data dimension by employing the Gaussian probability density function (GPDF) and named Gaussian vote feature selection (GVFS). To evaluate the effectiveness of the proposed GVFS, we compared it with the other five filter feature selection methods by utilizing the PMDCM’s data. Additionally, Gaussian naive Bayes (GNB), k-nearest neighbor algorithm (k-NN), and support vector machine (SVM) are utilized for the construction of fault diagnosis models. Experimental results show that the proposed GVFS has a better diagnostic effect than the other five feature selection methods, and the average accuracy of fault diagnosis improves from 97.89% to 99.44%. This paper lays the foundation of fault diagnosis for PMDCMs and provides a novel filter feature selection method.

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A sound based method for fault detection with statistical feature extraction in UAV motors

Cited by 70 publications

References 29 publications

UAV Fault Detection Methods, State-of-the-Art

UAV Fault Detection Methods, State-of-the-Art

A Stethoscope for Drones: Transformers-Based Methods for UAVs Acoustic Anomaly Detection

A Novel Supervised Filter Feature Selection Method Based on Gaussian Probability Density for Fault Diagnosis of Permanent Magnet DC Motors

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