Fault Diagnosis Algorithm Based on Adjustable Nonlinear PI State Observer and Its Application in UAV Fault Diagnosis

Miao, Qing; Wei, Juhui; Wang, Jiongqi; Chen, Yuyun

doi:10.3390/a14040119

Cited by 10 publications

(4 citation statements)

References 28 publications

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“…Miao et al in [92] propose an adjastable nonlinear proportional integral state observer for fault diagnosis of a fixed wing unmanned aerial vehicle. This algorithm in combination with the parameter updating technique is used to improve the effect of the additive fault estimation in single sensor.…”

Section: Model-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: Model-based Methodsmentioning

confidence: 99%

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

Puchalski

Giernacki

2022

Drones

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“…The crossover operator indicates the probability of the crossover operation occurring in the test vectors. Usually the crossover operator is set to 0.9 or 1.0 [8]. (4) Maximum Evolutionary Algebra G The common termination condition for differential evolution is to set the maximum evolutionary algebra, which is expressed as the differential operation stops when the number of differential evolutionary algebra reaches the maximum evolutionary algebra, and outputs the current population vector.…”

Section: Parameters Of the Differential Evolutionary Algorithmmentioning

confidence: 99%

Research on Model Optimization Method Based on Differential Evolution

Wang,

2024

AETR

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Nowadays, industrial upgrading and transformation continues to deepen, based on the deep neural network model of the integration of the advantages of the application is more and more prominent. Rolling bearings can not be ignored as the existence of mechanical equipment. A convolutional neural network parameter optimization algorithm based on differential evolution is proposed. The loss function of the fault diagnosis model is taken as the objective function of the optimization algorithm, and the optimization model is mainly aimed at optimizing the training parameters such as the number of convolutional kernels, convolutional kernel size, and step size of the 3D convolutional neural network, and the optimal parameter setting values are derived. Compared with the pre-optimization fault diagnostic model, the prediction accuracy of the fault diagnostic model is improved by 0.35%, and the loss rate is decreased by 1.13%, and the obtained fault diagnostic model has higher diagnostic accuracy.

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“…In [15], the authors suggest a fault diagnosis algorithm based on adaptive nonlinear proportional-integral (PI) observer for continuous time system applied to a fixed-wing unmanned aerial vehicle. Their approach was evaluated through simulation.…”

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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Fault Diagnosis Algorithm Based on Adjustable Nonlinear PI State Observer and Its Application in UAV Fault Diagnosis

Cited by 10 publications

References 28 publications

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

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

Research on Model Optimization Method Based on Differential Evolution

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

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