A zero-shot fault detection method for UAV sensors based on a novel CVAE-GAN model

Chuanjiang Li1; Kai Luo2; Lei Yang3; Shaobo Li1; Haoyu Wang1; Xiangjie Zhang1; Zihao Liao1

2024

10.1109/JSEN.2024.3405630

IEEE Sensors Journal   影响因子和分区

2379-9153

Unmanned aerial vehicles (UAVs) have demonstrated remarkable versatility across a spectrum of missions, and their autonomous flight and real-time control heavily rely on on-board sensors. However, UAV sensors are susceptible to external cyberattacks and internal failures in complex environments, and the scarcity of real sensor fault data poses challenges for developing accurate detection models. This paper is the first to investigate the zero-shot fault detection (ZSFD) problem for UAV sensors, which considers the most challenging scenario where there are no fault data provided for model training. Therefore, a novel Convolutional Variational Autoencoder-Generative Adversarial Network (CVAE-GAN) is proposed, which is designed with a discriminator, and a generator consisting of two encoders and one decoder. First, a fixed-length sliding window is used on normal data of multiple sensors to construct multidimensional input samples, and the generator first learns the latent distribution of fault-free data in adversarial training. Then, the CVAE-GAN generates large reconstruction errors for unseen fault data in testing, which are used as anomaly scores to be compared with adaptive thresholds to achieve fine-grained detection of different sensor faults. Finally, comprehensive experimental results based on real sensor data from an UAV demonstrate the effectiveness of our method in performing ZSFD tasks with different sensor faults, especially in detecting bias, stuck, and impact faults in magnetometer, where the GM metrics outperform the compared models by approximately 7% on average, and the test time efficiency opens up the possibility of realizing online fault detection.

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ENGINEERING

1.State Key Laboratory of Public Big Data, Guizhou University, Guiyang, Guizhou, China
2.School of System Design and Intelligent Manufacturing (SDIM), Southern University of Science and Technology, Shenzhen, China
3.School of Mechanical Engineering, Guizhou University, Guiyang, Guizhou, China

Chuanjiang Li,Kai Luo,Lei Yang,et al. A zero-shot fault detection method for UAV sensors based on a novel CVAE-GAN model[J]. IEEE Sensors Journal,2024,PP(99).

Chuanjiang Li.,Kai Luo.,Lei Yang.,Shaobo Li.,Haoyu Wang.,...&Zihao Liao.(2024).A zero-shot fault detection method for UAV sensors based on a novel CVAE-GAN model.IEEE Sensors Journal,PP(99).

Chuanjiang Li,et al."A zero-shot fault detection method for UAV sensors based on a novel CVAE-GAN model".IEEE Sensors Journal PP.99(2024).