The telemetry data obtained from an on-orbit spacecraft contain important information to indicate anomaly of the spacecraft. However, the large number of monitoring variables and the large amount of data points, as well as the lack of prior knowledge about anomaly due to complicated structure of spacecraft and its working conditions, pose great challenge to the anomaly detection. This article proposes an anomaly detection algorithm based on a spatial–temporal generative adversarial network (GAN) for the anomaly detection in telemetry data. This algorithm establishes a GAN-based model combining convolutional neural network (CNN) and long short-term memory (LSTM) to extract spatial and temporal features of the telemetry data, which facilitates the automatic and simultaneous representation of nonnegligible time-related characteristics of a monitoring variable and complex correlation between variables. Using these features, many kinds of anomalies including multivariate anomalies and contextual anomalies can be detected. Moreover, an anomaly score specifically designed to fit the GAN-based algorithm is also proposed to evaluate the possibility of anomaly by weighted fusion of the generator metric and the discriminator metric, which is proved to be significantly helpful to the accuracy of anomaly detection. Finally, experiments on one real telemetry data set and two public telemetry data sets are conducted, by which the proposed anomaly algorithm is demonstrated to be effective and accurate in detecting outliers in telemetry data. Comparison with three other state-of-the-art methods also reveals the advantages of our proposed algorithm.

中文翻译：

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时空生成对抗网络的遥测数据基航天器异常检测

从在轨航天器获得的遥测数据包含指示航天器异常的重要信息。然而，由于航天器结构及其工作条件的复杂性，大量的监控变量和大量的数据点，以及缺乏关于异常的先验知识，对异常检测提出了巨大的挑战。本文提出了一种基于时空生成对抗网络（GAN）的异常检测算法，用于遥测数据中的异常检测。该算法建立了一个基于GAN的模型，该模型结合了卷积神经网络（CNN）和长短期记忆（LSTM）来提取遥测数据的时空特征，这有助于自动且同时表示监视变量的不可忽略的时间相关特性以及变量之间的复杂相关性。使用这些功能，可以检测多种异常，包括多元异常和上下文异常。此外，还提出了专门设计为适合基于GAN的算法的异常评分，以通过生成器指标和鉴别指标的加权融合来评估异常的可能性，这被证明对异常检测的准确性有很大帮助。最后，对一个真实的遥测数据集和两个公开的遥测数据集进行了实验，证明了所提出的异常算法在检测遥测数据中的异常值时是有效且准确的。