HVDC Transmission Line Fault Identification: A Learning Based UAV Control Strategy

Khan, Waheed Aftab; Hamid, Shahzaib; Usman, Muhammad Rehan; Raza, Ali; Usman, Muhammad Arslan; Politis, Christos; Satrya, Gandeva Bayu

doi:10.1109/access.2022.3222566

Cited by 4 publications

(2 citation statements)

References 51 publications

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“…With the progress of science and technology, Unmanned Aerial Vehicles (UAVs) are also becoming more and more popular, and the scope of their use has been expanded from military applications to various industries such as aerial photography, cargo transport, and agricultural cultivation (Abdelmaksoud et al 2020;Khan et al 2022). The effective structure of UAV enables it to replace humans in some difficult or even dangerous conditions to complete a variety of tasks (Chen 2018), at the same time, its components will be subjected to a certain amount of fatigue damage, which affects the reliability of its operation (Peng et al 2024).…”

Section: Introductionmentioning

confidence: 99%

A multi-objective UAV fault diagnosis framework based on attention joint multi-spatial shared knowledge

An,

Li,

Zhang

et al. 2024

Preprint

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In recent years, unmanned aerial vehicles (UAVs) have had excellent performance in various fields, but their frequent component faults often lead to damages and serious accidents, so it is crucial to carry out timely fault diagnosis for them. Deep learning is widely used in the field of UAV fault diagnosis due to its superior feature extraction capability, but the increasing complexity of UAV faults and the scarcity of data have limited the development of deep learning in this field. To address the above problems, this paper proposed an Attention-based Joint Multi-Spatial Shared Knowledge Network (A-MSKN) for multi-objective fault diagnosis of UAVs under small samples. A-MSKN considers both complementary relationships between different tasks and intra-task dependencies within the same task for individual fault samples in different time intervals. Firstly, a single fault sample is divided into multiple sub-samples based on different time slices, and different sub-samples are coded to obtain different feature sub-spaces. Then, a sharing unit based on attention is designed to share not only the different feature subspaces within a task but also the features related between different tasks in a more fully shared way, to obtain more fault information for fault diagnosis under small samples. Finally, the effectiveness of the A-MSKN in the case of small samples was verified by testing it on real faulty flight data.

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Section: Introductionmentioning

confidence: 99%

A multi-objective UAV fault diagnosis framework based on attention joint multi-spatial shared knowledge

An,

Li,

Zhang

et al. 2024

Preprint

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“…With the continuous evolution of modern technology, Unmanned Aerial Vehicles (UAVs) have been widely applied in every walk of life, such as aerial photography, cargo transportation as well as agricultural planting [1], [2]. The simple and effective structure of UAVs enables them to complete varieties of missions in complicated operation conditions.…”

Section: Introductionmentioning

confidence: 99%

A Novel Dual Attention Convolutional Neural Network Based on Multisensory Frequency Features for Unmanned Aerial Vehicle Rotor Fault Diagnosis

Jiang,

Yu,

et al. 2023

IEEE Access

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By virtue of their convenience, reasonable cost and high efficiency, Unmanned Aerial Vehicles (UAVs) have been widely applied in every aspect of life. However, complicated operating conditions are prone to causing mechanical failure in UAVs, especially the rotor fault. Therefore, a novel dual attention convolutional neural network based on multisensory frequency features is proposed for UAV rotor fault diagnosis in this study. Firstly, according to the collected multisensory acceleration vibration signals of UAV rotors, time and frequency features in different health states (normal, rotor broken and crack fault) are compared and analyzed in detail. Secondly, a novel dual attention mechanism is proposed to not only focus on the effect of different sensors but also different frequency features of UAV. Moreover, it could adaptively assign larger weight to more important features to improve the fault diagnosis accuracy. Finally, a one-dimension convolutional neural network is adopted to extract the feature of signals and implement rotor fault diagnosis of UAV. The results derived from experimental signals demonstrate the superiority of the proposed method by comparison study. Additionally, it is found that the fault diagnosis accuracy of frequency features as input is much higher than that of time features and single frequency features as input.INDEX TERMS Attention mechanism, convolutional neural network, rotor fault diagnosis, unmanned aerial vehicle.

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IoT Integrated Transmission Line Fault Detection Using Cloud Server from Remote Location

Roy,

Basu,

Saha

et al. 2024

Lecture Notes in Networks and Systems

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HVDC Transmission Line Fault Identification: A Learning Based UAV Control Strategy

Cited by 4 publications

References 51 publications

A multi-objective UAV fault diagnosis framework based on attention joint multi-spatial shared knowledge

A multi-objective UAV fault diagnosis framework based on attention joint multi-spatial shared knowledge

A Novel Dual Attention Convolutional Neural Network Based on Multisensory Frequency Features for Unmanned Aerial Vehicle Rotor Fault Diagnosis

IoT Integrated Transmission Line Fault Detection Using Cloud Server from Remote Location

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