As spacecraft send back increasing amounts of telemetry data, improved anomaly detection systems are needed to lessen the monitoring burden placed on operations engineers and reduce operational risk Current spacecraft monitoring systems only target a subset of anomaly types and often require costly expert knowledge to develop and maintain due to challenges involving scale and complexity We demonstrate the effectiveness of Long Short-Term Memory (LSTMs) networks, a type of Recurrent Neural Network (RNN), in overcoming these issues using expert-labeled telemetry anomaly data from the Soil Moisture Active Passive (SMAP) satellite and the Mars Science Laboratory (MSL) rover, Curiosity We also propose a complementary unsupervised and nonparametric anomaly thresholding approach developed during a pilot implementation of an anomaly detection system for SMAP, and offer false positive mitigation strategies along with other key improvements and lessons learned during development

Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding

As spacecraft send back increasing amounts of telemetry data, improved anomaly detection systems are needed to lessen the monitoring burden placed on operations engineers and reduce operational risk. Current spacecraft monitoring systems only target a subset of anomaly types and often require costly expert knowledge to develop and maintain due to challenges involving scale and complexity. We demonstrate the effectiveness of Long Short-Term Memory (LSTMs) networks, a type of Recurrent Neural Network (RNN), in overcoming these issues using expert-labeled telemetry anomaly data from the Soil Moisture Active Passive (SMAP) satellite and the Mars Science Laboratory (MSL) rover, Curiosity. We also propose a complementary unsupervised and nonparametric anomaly thresholding approach developed during a pilot implementation of an anomaly detection system for SMAP, and offer false positive mitigation strategies along with other key improvements and lessons learned during development.

Detecting an anomaly is not only important for many terrestrial applications on Earth but also for space applications. Especially, satellite missions are highly risky because unexpected hardware and software failures can occur due to sudden or unforeseen space environment changes. Anomaly detection and spacecraft health monitoring systems have heavily relied on human expertise to investigate whether they are a true anomaly or not. Also, it is practically infeasible to produce labels on data due to the enormous amount of telemetries generated from a satellite. In this work, we propose a data-driven anomaly detection algorithm for Korea Multi-Purpose Satellite 2 (KOMPSAT-2). We develop a Multivariate Convolution LSTM with Mixtures of Probabilistic Principal Component Analyzers, where our approach uses both neural networks and probabilistic clustering to improve the anomaly detection performance. We evaluated our approach with a total of 22 million telemetry samples collected for 10 months from KOMPSAT-2. We also compare our approach with other state-of-the-art approaches. We show that our proposed approach is 35.8% better in precision, and 18.2% better in F-1 score than the best baseline approach. We plan to deploy our algorithm in the second half of 2019 to actually apply real operation of KOMPSAT-2.

Detecting Anomalies in Space using Multivariate Convolutional LSTM with Mixtures of Probabilistic PCA

Data preprocessing for heart disease classification: A systematic literature review.

Spacecrafts are critical systems that have to survive space environment effects. Due to its complexity, these types of systems are designed in a way to mitigate errors and maneuver the critical situations. Spacecraft delivers to the ground operator an abundance data related to system status telemetry; the telemetry parameters are monitored to indicate spacecraft performance. Recently, researchers proposed using Machine Learning (ML)/Telemetry Mining (TM) techniques for telemetry parameters forecasting. Telemetry processing facilitates the data visualization to enable operators understanding the behavior of the satellite in order to reduce failure risks. In this paper, we introduce a comparison between the different machine learning techniques that can be applied for low earth orbit satellite telemetry mining. The techniques are evaluated on the bases of calculating the prediction accuracy using mean error and correlation estimation. We used telemetry data received from Egyptsat-1 satellite including parameters such as battery temperature, power bus voltage and load current. The research summarizes the performance of processing telemetry data using autoregressive integrated moving average (ARIMA), Multilayer Perceptron (MLP), Recurrent Neural Network (RNN), Long Short-Term Memory Recurrent Neural Network (LSTM RNN), Deep Long Short-Term Memory Recurrent Neural Networks (DLSTM RNNs), Gated Recurrent Unit Recurrent Neural Network (GRU RNN), and Deep Gated Recurrent Unit Recurrent Neural Networks (DGRU RNNs).

Machine Learning Methods for Spacecraft Telemetry Mining

This paper presents a new unsupervised anomaly detection approach for spacecraft based on normal behavior clustering. This method takes as input a set of unlabelled historical telemetry data and automatically detects anomalies within the data. After these abnormal data are removed, the method constructs system normal behavior model based on normal data clustering. Then at run-time, it monitors the status of the spacecraft and detects any anomalies appearing in the real-time telemetry data by checking deviations from the normal behavior model. The experimental results show that the method is efficient and practical for anomaly detection of spacecraft system.

An Unsupervised Anomaly Detection Approach for Spacecraft Based on Normal Behavior Clustering

Development of intelligent fault detection and diagnosis technologies for spacecraft is one of important issues in the space engineering. In this paper, we present a new fault detection and diagnosis approach for spacecraft based on Principal Component Analysis (PCA) and Support Vector Machines (SVM). Firstly, PCA is used to extract features from input data and reduce the input data to low dimensional feature vectors. Then the method use a binary SVM to detect whether there is a fault or not. If the fault is detected, a multi-class SVM is used to identify fault type. The experimental results show that the method is efficient and practical for fault detection and diagnosis of spacecraft system.

Fault detection and diagnosis for spacecraft using principal component analysis and support vector machines

According to the requirement of state trend prediction for spacecraft fault prediction, a spacecraft state trend prediction method is proposed based on BP neural network. The principle and model of BP neural network are introduced into spacecraft fault prediction. Considering the specific application background, the relevant algorithm flow is provided. Taking the temperature parameter of key components in satellite as research object, the state trend prediction computation and comparison are implemented. The precision of the prediction results is evaluated, and it verifies the reliability and validity of the proposed method in quantitative way.

State trend prediction of spacecraft based on BP neural network

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Papers

An Unsupervised Anomaly Detection Approach for Spacecraft Based on Normal Behavior Clustering

Fault detection and diagnosis for spacecraft using principal component analysis and support vector machines

State trend prediction of spacecraft based on BP neural network