## 2 Background and Related Works
### Time Series Deep Learning Anomaly Detection  
The key problem of spacecraft health management and fault prediction is anomaly detection for time series data. Since the beginning of aerospace development in various countries, NASA, ESA, and China have collected a large number of spacecraft failure data in different fields, including remote sensing satellites, communications satellites, navigation satellites, deep space exploration missions, and manned space missions [33]. Spacecraft failure data is essentially a time series of multiple channels. It is helpful to improve the quality and reliability of a spacecraft by analyzing and modeling the historical time series data of spacecraft. The key role of time series analysis is to detect early anomalies in order to avoid major failures that lead to the loss of mission capability. Expert system and nearest neighbor method were used for anomaly detection in early spacecraft. NASA uses the Inductive Monitoring System (IMS) on the International Space Station and the Space Shuttle. IMS uses a clustering algorithm to detect spacecraft anomalies. In this algorithm, the abnormal states will fall outside the point clusters formed by clustering [34]. For fault prediction, a time series prediction method based on the ARIMA model is also developed [35]. With the development of space technology, these early anomaly detection methods can no longer meet the needs. Non-data-driven detection methods will consume a lot of manpower and material resources in the process of identifying and labeling anomalies. Therefore, anomaly detection based on deep learning time series has entered the field of spacecraft health management. Deep learning is a research direction in the field of machine learning. It extracts the internal rules and correlations of data through a hierarchical structure and simulates the mapping relationship between input data and output data in the real world. In recent years, deep learning has shown strong ability in the process of modeling and learning high-dimensional data, time series data, spatial data, and image data, and promoted the progress of various task boundaries. For example, SincNet has improved the understanding and processing of speech signals due to its powerful one-dimensional signal feature extraction ability since it was proposed [15]. Deep learning for anomaly detection, referred to as deep anomaly detection, aims to detect anomalies by learning feature representations or anomaly scores through neural networks. Following reference work [36] and based on the idea of enhancing the reconstruction ability of normal data, works [37; 38] developed detection methods with stronger performance (sequences and videos, respectively). Both of them set memory modules to store normal data and improve the fitting and reconstruction ability of normal data so as to strengthen the reconstruction error of abnormal data and enhance the performance of anomaly detection of the model. RNN and LSTM structures in deep learning have natural advantages for the prediction and reconstruction of time series data [37]. there have been a lot of works using RNN [39; 40; 41; 42] and LSTM [43; 44; 45; 46; 47] models for anomaly detection. LSTM is a kind of RNN network, which can well solve the problems of gradient disappearance and gradient explosion, which are easily generated in ordinary RNN, and can perform better in long sequences. Reference work [45] proposes an Encoder-decoder scheme based on LSTM, which learns and reconstructs normal time series, and uses the reconstruction error to detect anomalies. Reference work [44] designed a C-LSTM method to extract deeper and more complex long-period features by combining a convolutional neural network CNN, long short-term memory network LSTM, and deep neural network DNN.