# A Review of Anomaly Detection in Spacecraft Telemetry Data
## 3 State of the Art Anomaly Detection for Spacecraft Telemetry Data
### Artificial Intelligence Methods
#### 3.2.2 Deep Learning Methods  
With the remarkable growth of DL, a significant interest has also been noted in exploring these advanced algorithms for anomaly detection in space operations. Convolutional Neural Networks (CNNs) [8; 9; 10; 30], Recurrent Neural Networks (RNNs) [20; 22; 36], and Transformers are different classes of DL models, which have been used recently to detect anomalies in spacecraft telemetry data [31; 32; 53]. Convolutional Neural Networks (CNNs): CNNs consist of consecutive convolutional, pooling, and fully connected layers that adaptively learn the spatial hierarchies of features from the input sequence data. These architectures have achieved leading performance on various spacecraft anomaly detection tasks. For example, [PERSON] et al. [54] explored a specific CNN method called Temporal Convolution Network (TCN) to detect anomalies in multi-dimensional spacecraft telemetry data. The performance of TCN was evaluated using a set of real telemetry datasets of solid state power amplifiers which were generated from two different satellites. The telemetry variables of the first satellite include voltages and currents, while the variables provided by the second satellite involve temperatures and currents. The TCN model performed satisfactory detection performance, obtaining an average accuracy of 96% for short time step and 96.45% for long time steps, in addition to a smaller inference time in both cases. Another anomaly detection framework for spacecraft telemetry data based on TCN was proposed in [7]. This novel method utilizes dynamic graph attention to model the complex correlation among variables and time series. It outperformed state-of-the-art models in terms of \(F_{1}\) score, precision, and recall on SMAP/MSL spacecraft datasets. The Graph Convolutional Network (GCN) is another attempt, proposed in [55], to detect anomalies in spacecraft telemetry data. The results obtained on SMAP/MSL spacecraft datasets support the capability of the GCN in detecting anomalies in telemetry data. Inspired by the success of the GCN in spacecraft anomaly detection, [PERSON] et al. [30] developed another GCN variant. The novel method extracts the correlation information between the telemetry variables and provides attention for subsequent telemetry data anomaly detection. It was tested on SMAP/MSL datasets as well, and it obtained a 94.86% precision result and 99.59% recall result, which superseded the results obtain in [55]. The work in [8] discusses other state of-the-art CNN architectures such as Residual Networks (ResNet) and the Fully Convolutional Network (FCN). The latter can effectively process time series data, capturing valuable historical information for future prediction. Experimental results, conducted on the typical SMAP and MSL datasets, have proved that the ResNet model presents high performance, taking into account accuracy and computational efficiency, while the FCN shows a marked proficiency in capturing spatial patterns. Recurrent Neural Networks (RNNs): RNNs present another class of neural networks, which are used to process sequential data. Unlike CNN models, RNNs have connections that loop back, allowing them to maintain the memory of previous inputs in the sequence. Long Short Term Memory (LSTM) [20; 22; 36] and Gated Recurrent Unit (GRU) [8; 56] are the most known RNN architectures. Both of them use gates to learn the relationship between past and current data values. In addition to spatial components, RNNs (LSTMs and GNUs) show a noted proficiency in capturing temporal anomalies in spacecraft telemetry data. LSTMs were designed to fix the vanishing gradient problem inherent in the original RNN architecture, making them able to learn from long-range sequences. There exist many LSTM models which have been trained specifically to process spacecraft telemetry data. For example, the study originally proposed by [PERSON] et al. in [20] showed the utility of LSTMs in detecting abnormal behavior in real time series data.