| Citation: | ZHANG Keming, CAI Yuanwen, REN Yuanet al. Space anomaly events detection approach based on generative adversarial nets[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(7): 1329-1336. doi: 10.13700/j.bh.1001-5965.2018.0682(in Chinese)
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Anomaly events detection (AED) is quite important in space field for the complex space environment, difficult technology, high risk and strictly safe and reliable requirements. Since there are few space anomaly events samples and they are hard to obtain, it is necessary to carry out targeted AED. In order to prevent space accidents and find anomaly events that may lead to fault as soon as possible, a novel approach for space anomaly events detection based on generative adversarial nets (GAN) is proposed in this paper. Normal event samples are generated by normal GAN, anomaly event samples are generated by anomaly GAN. We proposed a reasonable algorithm to calculate the divergence of Euclidean distance between input events and simulated normal events generated by normal GAN, and Euclidean distance between input events and simulated abnormal events generated by anomaly GAN.As a result, abnormal events is detected accurately. The method is trained and tested using the Mixed National Institute of Standards and Technology (MNIST) database. The test results show that the key technical indexes, such as precision rate and recall rate of comprehensive evaluation index (F1) and precision recall curve (PRC), are at least 31% and 11% higher than the traditional variational autoencoder (VAE) method. In addition, we evaluated the method by collected data in real environment which simulated space audio data. The abnormal event detection performance is very good, which proved that the proposed method could detect anomaly event in real environments.
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