# 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
This method, which resulted in a precision and recall of 87.5% and 80.0%, respectively, on the NASA SMAP and MSL datasets, was improved upon in [57] by integrating Causality Features (CFs). In contrast to the classic LSTM, the CF-LSTM model treats the correlation between the parameters, which helps to enhance the prediction precision and is sensitive to anomalies. Using 20 parameters generated by a public dataset from a real system, CF-LSTM showed an improved detection precision, recall, and \(F_{1}\) score compared to LSTM and other anomaly detection models. Transfer learning [22], adapted to construct a general pretrained model to any specific telemetry channel, was shown to be effective in reducing the training time on the NASA SMAP/MSL dataset when it was integrated with the LSTM model. The work in [58] shows that a combination between the LSTM model and an attention multi-scale anomaly detection strategy was effective at detecting anomalies on NASA SMAP/MSL benchmark spacecraft data and the hydrogen clock data of the Beidou Navigation Satellite. The multi-scale anomaly detection strategy integrates the detection of global and local features at different time scales which provides higher recall rate and fewer false alarms. In [59], two methodologies that integrated the Bayesian neural networks and the Variational Auto-Encoder (VAE) within the LSTM (MCD-BiLSTM, MCD-BiLSTM+VAE) have been proposed for detecting anomalies. The experimental results on an imbalanced spacecraft telemetry dataset show that the proposed models can obtain higher scores than traditional neural networks. GRU is another RNN model designed to process temporal features within sequential data. This model, which is built to capture dependencies for sequences of varied lengths, has a simpler structure compared to LSTM. This advantage offers the capacity to model data sequences with reduced computational requirements. The GRU model [7] was trained on the SMAP/MSL datasets to handle the anomaly detection problem. It showed a sufficient performance/training time tradeoff with respect to the conventional methods. GRU method leveraged the benefits of the Extreme Value Theory (EVT) in [56] to process multiple channels of telemetry data and maximize the detection performance. [PERSON] et al. [60] evaluated an anomaly detection approach based on a Variational Auto-Encoder with a Gated Recurrent Unit (GRU-VAE) for anomaly detection and diagnosis of spacecraft system. The GNU-VAE showed lower anomaly detection results on the SMAP/MSL datasets than the other baseline methods. Transformers: Exploring the self-attention mechanism, transformer-based anomaly detectors have achieved great improvements in time series tasks [8; 29; 31; 32; 53]. This success is attributed to their great power of discovering reliable long-range temporal dependencies. The experimental results in [29; 32] demonstrated that the different transformer-based architectures present promising results in computational efficiency. For example, they could save about 80% of the training time compared to LSTM. They have the advantage that they can focus on both local and global contexts, especially in scenarios where anomalies are subtle and span over longer durations. In [31], [PERSON] et al. have proposed a transformer based encoder-decoder model called TranAD able to identify data trends with limited data. The novel approach, tested on eight different spacecraft datasets, managed to improve the \(F_{1}\) score by 11% with a limited training dataset and identify up to 75% of the detected anomalies, which was higher than the reference methods. In [53], a transformer-based regression model was used to analyze one-dimensional time series data of a specific spacecraft parameter. Experimental results demonstrate that this approach surpasses some ML methods like Random Forest and SVM.