# A Review of Anomaly Detection in Spacecraft Telemetry Data
## 3 State of the Art Anomaly Detection for Spacecraft Telemetry Data
### Artificial Intelligence Methods
#### 3.2.1 Machine Learning Methods
Dimensionality Reduction (DR): DR approaches have emerged as valuable tools for identifying anomalies. The key idea of DR methods [47] is representing the principal system behavior of the spacecraft telemetry data by a much smaller number of latent variables. Recovering the original dimensions of the reduced data system and compare them with the original one has proven to be useful in detecting anomalies. Principal Component Analysis (PCA) [36; 48; 49], Kernel PCA (KPCA) [48], Mixture Probabilistic PCA (MPPCA) [50], and the Laplacian Eigenmaps Latent Variable Model (ELVM), among others, have been used to identify spacecraft anomalies. These methods have been assessed using different real spacecraft telemetry datasets; some of them are anonymized like in [49], while the others are provided by the EPS data of two satellites owned by JAXA [47; 48] or the Korea Multi-Purpose Satellite 2 (KOMPSAT-2) [50]. Considering the results obtained in [47], MPPCA was considered as the best performing among the reference methods (PCA, KPCA, LELVM, etc.) both in the detection accuracy and computational time. Neural Networks (NN): There are some works which utilized neural networks to detect anomalies in spacecraft telemetry data. For example, an NN-based method was assessed in [37], and it gave an accuracy of 92%. [PERSON] et al. [51] proposed an improved NN method called Envelop Learning and Monitoring using Error Relaxation (ELMER). Thismethod uses the NN to periodically set new threshold bounds, providing faster detection with fewer false alarms. [PERSON] et al. [52] proposed an Anomaly Detection via Topological-feature Map (ADTM)-based approach. The ADTM method combines a two-layer Artificial Neural Network (ANN) and the Extra Tee Classifier (ETC) approach. The performance of ADTM was validated on telemetry data collected from a Lab-Stationed CubeSat (LabSat), and it proved its performance at detecting both known and unknown anomalies. Extreme Learning Machines (ELMs) are another NN model, introduced by [PERSON] et al. in [27], to detect any abnormalities in the behavior of the channels. The current approach is able to learn from the time series data in real time. It lessens the amount of training and data required to obtain a predictor model for each channel. Experiments conducted on the SMAP/MSL dataset have proved that this method is able to achieve comparable performance to the state-of-the-art spacecraft anomaly detection methods with minimal training time and data. In addition to one-dimensional data, ML methods can support multi-dimensional data. Indeed, these methods perform very well when the size of data is small. However, the above models have suffered considerable challenges due to the increasing volumes of complex telemetry data, which limits the detection performance, results in high false detection rates, and does not allow for the efficient interpretation of anomaly detection of telemetry data. Recently, there has been a shift in interest toward deep learning (DL) methods instead of ML approaches due to their capacity to handle the large volume of data and the complex interactions among multi-variate spacecraft telemetry data.