Satellite Health Monitoring Research Articles

Anomaly detection in telemetry data using a jointly optimal one-class support vector machine with dictionary learning

Anomaly detection based on telemetry data is a major issue in satellite health monitoring, given that it can identify unusual or unexpected events to avoid serious accidents and ensure the safety and reliability of operations. This study proposes a jointly optimal one-class support vector machine with dictionary learning (JODL-OC) framework for multivariate anomaly detection in telemetry. Dictionary learning (DL) extracts the correlation and local dynamics of time series into a sparse matrix, and the one-class support vector machine (OCSVM) detects anomalies in the transformed sparse space. The optimisation objective of DL is modified by introducing a novel regularisation term called soft-boundary OCSVM, so that the JODL-OC seamlessly integrates DL, sparse representation, and classifier training into a unified model to jointly learn the features and decision boundary. Furthermore, we propose an iterative approach to solve the optimisation function and obtain the optimal JODL-OC framework for anomaly detection. A case analysis based on satellite antenna telemetry data reveals that the proposed method improves the detection precision, F1-score, FPR and FAR compared to other existing anomaly detection methods, particularly to illustrate the superior performance in correlation anomalies.

Anomaly Detection in Satellite Telemetry Data Using a Sparse Feature-Based Method

Anomaly detection based on telemetry data is a major issue in satellite health monitoring which can identify unusual or unexpected events, helping to avoid serious accidents and ensure the safety and reliability of operations. In recent years, sparse representation techniques have received an increasing amount of interest in anomaly detection, although its applications in satellites are still being explored. In this paper, a novel sparse feature-based anomaly detection method (SFAD) is proposed to identify hybrid anomalies in telemetry. First, a telemetry data dictionary and the corresponding sparse matrix are obtained through K-means Singular Value Decomposition (K-SVD) algorithms, then sparse features are defined from the sparse matrix containing the local dynamics and co-occurrence relations in the multivariate telemetry time series. Finally, lower-dimensional sparse features vectors are input to a one-class support vector machine (OCSVM) to detect anomalies in telemetry. Case analysis based on satellite antenna telemetry data shows that the detection precision, F1-score and FPR of the proposed method are improved compared with other existing multivariate anomaly detection methods, illustrating the good effectiveness of this method.

A deep auto-encoder satellite anomaly advance warning framework

PurposeThe purpose of this paper is to ensure the stable operation of satellites in orbit and to assist ground personnel in continuously monitoring the satellite telemetry data and finding anomalies in advance, which can improve the reliability of satellite operation and prevent catastrophic losses.Design/methodology/approachThis paper proposes a deep auto-encoder (DAE) satellite anomaly advance warning framework for satellite telemetry data. Firstly, this study performs grey correlation analysis, extracts important feature attributes to construct feature vectors and builds the variational auto-encoder with bidirectional long short-term memory generative adversarial network discriminator (VAE/BLGAN). Then, the Mahalanobis distance is used to measure the reconstruction score of input and output. According to the periodic characteristic of satellite operation, a dynamic threshold method based on periodic time window is proposed. Satellite health monitoring and advance warning are achieved using reconstruction scores and dynamic thresholds.FindingsExperiment results indicate DAE methods can probe that satellite telemetry data appear abnormal, trigger a warning before the anomaly occurring and thus allow enough time for troubleshooting. This paper further verifies that the proposed VAE/BLGAN model has stronger data learning ability than other two auto-encoder models and is sensitive to satellite monitoring data.Originality/valueThis paper provides a DAE framework to apply in the field of satellite health monitoring and anomaly advance warning. To the best of the authors’ knowledge, this is the first paper to combine DAE methods with satellite anomaly detection, which can promote the application of artificial intelligence in spacecraft health monitoring.

Missing Telemetry Data Prediction Algorithm via Tensor Factorization

Satellite health monitoring is an important concern for satellite security, for which satellite telemetry data is the only source of data. Therefore, accurate prediction of missing data of satellite telemetry is an important forward-looking approach for satellite health diagnosis. For the high-dimensional structure formed by the satellite multi-component system, multi-instrument and multi-monitoring index, the Tensor Factorization based Prediction (TFP) algorithm for missing telemetry data is proposed. The proposed algorithm surpasses most existing methods, which can only be applied to low-dimensional data or specific dimension. The proposed algorithm makes accurate predictions by modeling the telemetry data as a Tensor to integrally utilize its high-dimensional feature; Computing the component matrixes via Tensor Factorization to reconstruct the Tensor which gives the predictions of the missing data; An efficient optimization algorithm is proposed to implement the related tensor calculations, for which the optimal parameter settings are strictly theoretically deduced. Experiments show that the proposed algorithm has better prediction accuracy than the most existing algorithms.

High‐performance antenna for energy harvesting in satellite health monitoring system

This study is targeted towards designing and analysing the X and Ku bands receiving antenna using the Code Blocks software and Advanced Design System simulation tool for energy harvesting in order to satisfy the energy needs of autonomous wireless sensors on board of geostationary satellite for structural health monitoring of the satellite panels, where weight, cost, size and efficiency are the principal space antenna parameters accounted. The energy harvester generally consists of receiving antenna, which harvests and scavenges the circumambient electromagnetic field and inputs the rectifier in order to power the sensors. In this study, a compact, wideband semi-ellipse patch antenna backed with defected ground structure is presented. The antenna compactness (186.8 mm3) is obtained by truncating the elliptical patch to form a semi-ellipse patch without compromising the band of interest which reduces from launching weight and cost. The proposed antenna covers X and Ku bands for voltage standing wave ratio ≤2 with a maximum return loss of 28.75 dB, the efficiency of 95%, the gain of 9.6 dBi and directivity of 14 dBi, which makes the proposed antenna compatible to use with the rectifier in order to satisfy the energy needs of wireless sensors.

Anomaly detection of satellite telemetry based on optimized extreme learning machine

In aerospace, anomaly detection based on telemetry data is a critical satellite health monitoring task that is important for identifying unusual or unexpected events and for taking measurements to improve system safety and avoid serious problems. This paper introduces a novel optimized predictive model for detecting anomalies using the Grey Wolf Optimization (GWO) algorithm and an Extreme Learning Machine (ELM), called GWO-ELM. The proposed GWO-ELM is used to find anomalous events by comparing the actual observed values with the predicted intervals of telemetry data; the GWO is applied to optimize the ELM’s input weights and the bias parameters of hidden neurons to improve its prediction accuracy and ability to detect anomalies. A performance evaluation of GWO-ELM is conducted on the NASA shuttle valve benchmark dataset, which contains samples of Labeled anomalies and various metrics are collected. The experimental results for GWO-ELM show that it makes predictions with high efficiency, is stable when detecting anomalies, and requires little computational time. In addition, the results of GWO-ELM compared with those of the basic ELM algorithm with randomized parameter selection and a support vector machine (SVM), demonstrate the effectiveness and superiority of the proposed model.

Machine learning in telemetry data mining of space mission: basics, challenging and future directions

The development of an intelligent artificial satellite health monitoring system is a key issue in aerospace engineering that determines satellite health status and failure using telemetry data. The modern design of data mining and machine learning technologies allows the use of satellite telemetry data and the mining of integrated information to produce an advanced health monitoring system. This paper reviews the current status and presents a framework of necessary processes on data mining to solving various problems in telemetry data such as error detection, prediction, summarization, and visualization of large quantities, and help them understand the health status of the satellite and detect the symptoms of anomalies. Machine learning technologies that include neural networks, fuzzy sets, rough sets, support vector machines, Naive Bayesian, swarm optimization, and deep learning are also presented. Also, this paper reviews a wide range of existing satellite health monitoring solutions and discusses them in the framework of remote data mining techniques. In addition, we are discussing the analysis of space debris flow analysis and the prediction of low earth orbit collision based on our orbital Petri nets model. Challenges to be addressed and future directions of research are identified and an extensive bibliography is also included.

Machine learning and Data Mining Techniques in Health Monitoring of Artificial Satellites Mission

The development of an intelligent satellite health monitoring system is an essential issue in aerospace engineering that determines the state of health and failure of satellites based on telemetry data. The recent development of data mining and machine learning techniques makes it possible to use satellite telemetry data and to extract integrated information for the production of advanced health monitoring system. The editorial paper can be used as a guideline for the design of an intelligent health monitoring solution based on remote data.

Multiband rectenna for microwave applications

Abstract This paper reports a multiband rectenna (rectifier + antenna) suitable for the electromagnetic energy harvesting of the spill-over loss of microwave antennas placed on board of geostationary satellites. Such rectenna is used for powering autonomous wireless sensors for satellite health monitoring. The topology of the rectenna is presented. The experimental results demonstrate that the proposed compact rectenna can harvest efficiently the incident electromagnetic energy at three different frequencies that are close to the resonant frequencies of the cross-dipoles implemented in the antenna array.

Microwave Power Harvesting for Satellite Health Monitoring

This paper addresses the microwave energy harvesting on board of geostationary satellites for health satellite monitoring. To prove the feasibility of such a concept, we investigated the electromagnetic environment existing on antenna panels. Based on established cartographic maps, three designs of rectennas are proposed. Measured dc powers ranging from 0.256 to 1.28 mW can be harvested for electric field levels ranging from 91 to 121 V/m and by using very simple and compact designs. The harvesting structures consist of only one Schottky diode per rectenna and present a total surface of 2.4 cm 2. They are suitable for powering the new generation of ultra-low power transceivers, thus enabling autonomous wireless power networks for satellite health monitoring.