Fault detection and isolation based on UKFs for a novel ducted fan UAV

Liu, Liyuan; Ma, Yue; Xu, Bugong; Chen, Xiang; Yang, Xingbang

doi:10.1109/aus.2016.7748049

Cited by 11 publications

(3 citation statements)

References 10 publications

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“…Liu et al [59] present unscented Kalman filter (UKF)-based fault detection and isolation for a ducted fan UAV. This type of drone consists of two main tandem contra-rotating ducted fans, two auxiliary ducted fans and two control vanes sets.…”

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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“…Based on unscented Kalman filters, an Unscented Multiple Model Adaptive Estimation (UMMAE) method was developed in [62]. The suggested approach offers a parallel bank of filters that are in charge for tracking the operating mode of the respective actuator.…”

Section: Actuators 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 fault detection method based on the suboptimal fading unscented Kalman filter (SFUKF) is designed in [22]. Liu et al [23] introduced an adaptive estimation method based on a bank of unscented Kalman filters (UKF) to monitor the actuator health in a UAV. In the literature [24], Guo et al utilized Extended Kalman Filter (EKF) and the UAV kinematics model to realize analytical redundancy.…”

Section: Introductionmentioning

confidence: 99%

Online Learning Based Underwater Robotic Thruster Fault Detection

Guo

Zhao

et al. 2021

Applied Sciences

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This paper presents a novel online learning-based fault detection designed for underwater robotic thruster health monitoring. In the fault detection algorithm, we build a mathematical model between the control variable and the propeller speed by fitting collected online work status data to the model. To improve the accuracy of online modeling, a multi-center PSO algorithm with memory ability is utilized to optimize the modeling parameters. Additionally, a model online update mechanism is designed to accommodate the model to the change of thruster work status and sea environment. During the operation, propeller speed of the underwater robot is predicted through the online learning-based model, and the model residuals are used for thruster health monitoring. To avoid false alarm, an adaptive fault detection strategy is established based on model online update mechanism. The proposed method has been extensively evaluated using different underwater robotics, through a sea trial data simulation, a pool test fault detection experiment and a sea trial fault detection experiment. Compared with fixed model-based method, speed prediction MAE of the online learning model is at least 37.9% lower than that of the fixed model. The online learning-based method show no misdiagnosis in experiments, while the fixed model-based method is misdiagnosed. Experimental results show that the proposed method is competitive in terms of accuracy, adaptability, and robustness.

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Fault detection and isolation based on UKFs for a novel ducted fan UAV

Cited by 11 publications

References 10 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

Online Learning Based Underwater Robotic Thruster Fault Detection

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