Research on Fault Detection and Diagnosis for Small Unmanned Aerial Vehicle

Yu, Ning; Xu, Xiaoying; Jiang, Zhengwu; Ning, Bin-Yao; Wang, Yong

doi:10.1515/9783110540048-054

Cited by 2 publications

(1 citation statement)

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“…The paper [31] presents the state of the art in fault detection and diagnosis for air dynamics by specifying the methods for SUAV pneumatics parameters and properties determining as well as the key FDD technologies analysis. Another effective method for detecting faults in a sensor network concerns the distribution of faults.…”

Section: Methods Of Fault Detectionmentioning

confidence: 99%

Survey of Applications of Machine Learning for Fault Detection, Diagnosis and Prediction in Microclimate Control Systems

et al. 2023

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An appropriate microclimate is one of the most important factors of a healthy and comfortable life. The microclimate of a place is determined by the temperature, humidity and speed of the air. Those factors determine how a person feels thermal comfort and, therefore, they play an essential role in people’s lives. Control of microclimate parameters is a very important topic for buildings, as well as greenhouses, where adequate microclimate is fundamental for best-growing results. Microclimate systems require adequate monitoring and maintenance, for their failure or suboptimal performance can increase energy consumption and have catastrophic results. In recent years, Fault Detection and Diagnosis in microclimate systems have been paid more attention. The main goal of those systems is to effectively detect faults and accurately isolate them to a failing component in the shortest time possible. Sometimes it is even possible to predict and anticipate failures, which allows preventing the failures from happening if appropriate measures are taken in time. The present paper reviews the state of the art in fault detection and diagnosis methods. It shows the growing importance of the topic and highlights important open research questions.

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Section: Methods Of Fault Detectionmentioning

confidence: 99%

Survey of Applications of Machine Learning for Fault Detection, Diagnosis and Prediction in Microclimate Control Systems

et al. 2023

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Actuator fault modeling and fault-tolerant tracking control of multi-vectored propeller aerostat

Dong

Yan

2022

Aeronaut. j.

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For a multi-vectored propeller aerostat with actuator faults, this study presents a fault-tolerant tracking control strategy, which includes fault modeling, observer, force estimation and tracking controller. Fault modeling considers the four types of faults of vectored propellers, namely, thrust offset, thrust efficiency loss, vectored angle offset and vectored angle stuck. Actuator faults can be determined from the fault observer, which identifies the thrust offset from the acceleration difference of the faulty aerostat with the ideal model. For tracking positions, a traditional PID controller is constructed with virtual control, compensated with the estimated fault force. The control allocation scheme is proposed to redistribute the available actuators in case faults occur. Simulation results of position tracking prove the effectiveness of the proposed strategy.

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Research on Fault Detection and Diagnosis for Small Unmanned Aerial Vehicle

Cited by 2 publications

References 0 publications

Survey of Applications of Machine Learning for Fault Detection, Diagnosis and Prediction in Microclimate Control Systems

Survey of Applications of Machine Learning for Fault Detection, Diagnosis and Prediction in Microclimate Control Systems

Actuator fault modeling and fault-tolerant tracking control of multi-vectored propeller aerostat

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