Research on UAV Wing Structure Health Monitoring Technology Based on Finite Element Simulation Analysis

Chen, Mingjie; Pan, Zhengqiang; Chi, Chengzhi; Jin, Ma; Hu, Fei; Wu, Jianmin

doi:10.1109/phm-jinan48558.2020.00022

Cited by 3 publications

(2 citation statements)

References 2 publications

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“…In [131], a Beacon Exception Analysis Method (BEAM) was applied to conduct fault detection on the data regarding UAV wing health and various damage states. The developed approach was verified thought finite element simulation analysis.…”

Section: Anomaly Detection In Uavsmentioning

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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Section: Anomaly Detection In Uavsmentioning

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 model-based anomaly detection method requires the establishment of an accurate physical model to describe the operating characteristics of the UAV for the purpose of identifying anomalous data. For example, Chen et al [5] used FLUENT and ANSYS software for finite element simulation analysis to determine the fault monitoring nodes, and finally used the beacon anomaly analysis method to detect anomalies in the data. Tan et al [6] introduced a model correction link to reduce the long-term cumulative error of the system in dynamic operation.…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection for Data from Unmanned Systems via Improved Graph Neural Networks with Attention Mechanism

Wang

et al. 2023

Drones

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Anomaly detection has an important impact on the development of unmanned aerial vehicles, and effective anomaly detection is fundamental to their utilization. Traditional anomaly detection discriminates anomalies for single-dimensional factors of sensing data, which often performs poorly in multidimensional data scenarios due to weak computational scalability and the problem of dimensional catastrophe, ignoring potential correlations between sensing data and some important information of certain characteristics. In order to capture the correlation of multidimensional sensing data and improve the accuracy of anomaly detection effectively, GTAF, an anomaly detection model for multivariate sequences based on an improved graph neural network with a transformer, a graph attention mechanism and a multi-channel fusion mechanism, is proposed in this paper. First, we added a multi-channel transformer structure for intrinsic pattern extraction of different data. Then, we combined the multi-channel transformer structure with GDN’s original graph attention network (GAT) to attain better capture of features of time series, better learning of dependencies between time series and hence prediction of future values of adjacent time series. Finally, we added a multi-channel data fusion module, which utilizes channel attention to integrate global information and upgrade anomaly detection accuracy. The results of experiments show that the average accuracies of GTAF, the anomaly detection model proposed in this paper, are 92.83% and 96.59% on two datasets from unmanned systems, respectively, which has higher accuracy and computational efficiency compared with other methods.

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Anomaly Detection of Fixed-Wing Unmanned Aerial Vehicle (UAV) Based on Cross-Feature-Attention LSTM Network

Xu,

Yang,

Wen

et al. 2023

Communications in Computer and Information Science

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Research on UAV Wing Structure Health Monitoring Technology Based on Finite Element Simulation Analysis

Cited by 3 publications

References 2 publications

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

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

Anomaly Detection for Data from Unmanned Systems via Improved Graph Neural Networks with Attention Mechanism

Anomaly Detection of Fixed-Wing Unmanned Aerial Vehicle (UAV) Based on Cross-Feature-Attention LSTM Network

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