Research on multi-classification method of UAV sensor fault based on wavelet entropy and AFWA-BP neural network

Chen, Yuepeng; Hu, Xia; Zhang, Qingyong; Zhang, Cong

doi:10.1109/yac.2017.7967526

Cited by 4 publications

(2 citation statements)

References 6 publications

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“…In a subsequent article, [106] Chen et al present another sensor fault detection algorithm using wavelet packet and back propagation neural network. Unlike the method described in [105], this time rely on adaptive fireworks to enhance the local search ability as well as to improve the convergence of the proposed algorithm.…”

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 similar methodology of wavelet entropy energy feature extraction was proposed in [41], in order to acquire the fault feature vector, as well as for updating the weight and threshold of the neural network the authors adopt the adaptive fireworks algorithm. Simulations demonstrate the accuracy and robustness of the AFWA-BP neural network.…”

Section: Sensors Fault Diagnosismentioning

confidence: 99%

A Survey on Fault Diagnosis and Fault-Tolerant Control Methods for Unmanned Aerial Vehicles

Fourlas

Karras

2021

Machines

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The continuous evolution of modern technology has led to the creation of increasingly complex and advanced systems. This has been also reflected in the technology of Unmanned Aerial Vehicles (UAVs), where the growing demand for more reliable performance necessitates the development of sophisticated techniques that provide fault diagnosis and fault tolerance in a timely and accurate manner. Typically, a UAV consists of three types of subsystems: actuators, main structure and sensors. Therefore, a fault-monitoring system must be specifically designed to supervise and debug each of these subsystems, so that any faults can be addressed before they lead to disastrous consequences. In this survey article, we provide a detailed overview of recent advances and studies regarding fault diagnosis, Fault-Tolerant Control (FTC) and anomaly detection for UAVs. Concerning fault diagnosis, our interest is mainly focused on sensors and actuators, as these subsystems are mostly prone to faults, while their healthy operation usually ensures the smooth and reliable performance of the aerial vehicle.

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A Survey on Fault Diagnosis Methods for UAVs

Fourlas

Karras

2021

2021 International Conference on Unmanned Aircraft Systems (ICUAS)

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Research on multi-classification method of UAV sensor fault based on wavelet entropy and AFWA-BP neural network

Cited by 4 publications

References 6 publications

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

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

A Survey on Fault Diagnosis and Fault-Tolerant Control Methods for Unmanned Aerial Vehicles

A Survey on Fault Diagnosis Methods for UAVs

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