Graph Attention Network-Based Fault Detection for UAVs With Multivariant Time Series Flight Data

He, Kai; Yu, Dakuan; Wang, Dong; Chai, Mengjuan; Lei, Shuntian; Zhou, Changlin

doi:10.1109/tim.2022.3219489

Cited by 11 publications

(5 citation statements)

References 26 publications

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“…Flight control system [13,[28][29][30] Low sampling frequency Vibration signals [31][32][33][34][35][36] Sensitive to mechanical faults, high cost Acoustic signals [37][38][39] Sensitive, inconvenient to install Temperature signals [27] Sensitive, high cost, inconvenient to install of the motor's operational status, and the convenience and low cost of the current signal collection are noteworthy. Moreover, the sensors demonstrate high integration levels, with many mid to high end electronic speed control systems incorporating built in current and voltage detection modules.…”

Section: Data Sourcesmentioning

confidence: 99%

“…Consequently, acquiring UAV fault data remains challenging. Traditional small sample fault diagnosis methods such as transfer learning [46], meta learning [47], and generative adversarial networks [30] necessitate complex processing techniques and significant computational resources. Moreover, authenticity concerns regarding virtual samples further complicate the situation.…”

Section: Data Sourcesmentioning

confidence: 99%

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Fault diagnosis of drone motors driven by current signal data with few samples

Chen,

Li,

et al. 2024

Meas. Sci. Technol.

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Multirotor UAVs find extensive application across diverse domains, with their motors serving as pivotal components in the UAV power system. The majority of UAV failures and crashes stem from motor malfunctions, underscoring the critical need for research in UAV motor fault diag-nosis. This paper focuses on quadrotor UAVs employing DC brushless motors as the research subject. The study designs fault experiments for three key components—stator, rotor, and bear-ing—based on the structural and operational characteristics of these motors. Addressing chal-lenges such as limited installation space for UAV payloads and redundant components, difficul-ties in sensor installation, high costs, and data acquisition complexities, this research adopts the current signal as the diagnostic signal. It explores a UAV motor fault diagnostic method based on the current signal. Furthermore, the study investigates a motor fault diagnosis method based on the current signal. To overcome challenges arising from the UAV's heightened sensitivity to the health of its components and the limited availability of fault data training samples, traditional machine learning and deep learning methods face difficulties in identifying representative fea-tures under small samples, often succumbing to overfitting risks and resulting in low diagnostic accuracy. To address these issues, a hybrid neural network fault diagnosis model is proposed, integrating the width learning system and convolutional neural network. The width learning system maps the original current signal into a feature space, yielding more robust and repre-sentative sample features. Subsequently, the convolutional neural network undertakes feature extraction and classification tasks. In experiments focused on current signal data-driven small-sample fault diagnosis in UAV motors, the proposed model outperforms other models used for comparison.

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Section: Data Sourcesmentioning

confidence: 99%

Section: Data Sourcesmentioning

confidence: 99%

Fault diagnosis of drone motors driven by current signal data with few samples

Chen,

Li,

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Recurrent neural network such as LSTM and GRU are being widely used in fault diagnosis due to their good time series data processing capability. He et al [27] proposed a graph attention-based GRU UAV fault diagnosis model and achieved better fault diagnosis performance in two major flight modes. Park et al [28] proposed a modelfree stacked LSTM fault detection method and successfully realized the diagnosis of fixed-wing UAV control surface failure faults.…”

Section: Introductionmentioning

confidence: 99%

Unmanned aerial vehicle fault diagnosis based on ensemble deep learning model

Huang,

Liang,

Dai

et al. 2024

Meas. Sci. Technol.

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To address the problems of external interference during unmanned aerial vehicle (UAV) flight and the low accuracy and weak generalization ability of the current single fault diagnosis model, this work proposes a weighted ensemble deep learning UAV fault diagnosis method. First, considering the differences in training methods and fault feature recognition principles of deep networks with different structures, three hybrid fault diagnosis models are constructed. These models are constructed by combining convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM) and bidirectional gate recurrent unit (BiGRU). CNN is used to extract the features of the UAV flight data and the obtained feature information is fed into BiLSTM and BiGRU to explore the fault information inherent in the time series data. Then, the three hybrid fault diagnosis models are used as the individual model of the ensemble learning algorithm, and the weights of the three individual models are optimized using a random grid search algorithm to construct a UAV fault diagnosis model based on hybrid deep learning weighted ensemble, which further improves the fault diagnosis performance. Finally, it is demonstrated experimentally that the proposed hybrid deep learning weighted ensemble fault diagnosis model can effectively identify the fault of UAV with an accuracy of 99.22% and 99.62% on binary and multivariate classification, respectively, and reflects better generalization performance in the metrics of precision, recall, and F1 score.

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“…In recent years, the hot research content such as sensor fault diagnosis(FD) [7,8], the fault tolerant control(FTC) [9,10] and prognostics and health management(PHM) [11,12] technology in the field of unmanned vehicles shows that the unmanned vehicle autopilot system is suffering from various types of faults, which revealing that the unmanned vehicle autopilot system still has considerable vulnerability. In order to efficiently verify the reliability of the autopilot system, a testing platform that can accomplish the fault simulation of the autopilot system has a very important role for the above research content.…”

Section: Introductionmentioning

confidence: 99%

A Chip-Level Testing Platform of Unmanned Vehicle Autopilot Systems with FPGA-Based Hardware-in-the-Loop Simulation

Dai,

Fang

2024

Preprint

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Nowadays, unmanned vehicles are widely used in various fields. However, the safety of unmanned vehicles is directly determined by the autopilot systems, and sensor faults are a very critical factor that affects the normal operation of the autopilot systems. To efficiently and comprehensively test unmanned systems for sensor faults, this paper proposes a chip-level testing platform for unmanned vehicle autopilot systems based on FPGA hardware-in-the-loop simulation. Unlike existing testing platforms, the introduction of FPGA technology allows us to achieve nanosecond real-time simulation frequency, thus realizing chip-level simulation of sensors and black-box automated testing. Firstly, we propose a set of general platform and modeling methods, and the paper presents a detailed application of the four most common necessary sensors in autopilot systems as examples. Secondly, we propose for the first time a chip-level fault testing method for unmanned systems, and build a test platform with a quadrotor vehicle, the PX4 software platform, and the Pixhawk4 hardware platform as examples. Finally, the experimental results verify that the testing platform proposed in this paper can adapt to various autopilot systems and has high simulation test reliability.

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Graph Attention Network-Based Fault Detection for UAVs With Multivariant Time Series Flight Data

Cited by 11 publications

References 26 publications

Fault diagnosis of drone motors driven by current signal data with few samples

Fault diagnosis of drone motors driven by current signal data with few samples

Unmanned aerial vehicle fault diagnosis based on ensemble deep learning model

A Chip-Level Testing Platform of Unmanned Vehicle Autopilot Systems with FPGA-Based Hardware-in-the-Loop Simulation

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