Exploiting Wavelet Recurrent Neural Networks for satellite telemetry data modeling, prediction and control

Napoli, Christian; Magistris, Giorgio De; Ciancarelli, Carlo; Corallo, Francesco; Russo, Francesco Paolo; Nardi, Daniele

doi:10.1016/j.eswa.2022.117831

Cited by 12 publications

(2 citation statements)

References 23 publications

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“…Zeng et al [28] proposed an anomaly detection framework using a causal network and feature-attention-based long short-term memory (CN-FA-LSTM) network, which is used to study causality in multivariate and large-scale telemetry data and is more sensitive to anomalies for prediction. Napoli et al [29] developed a wavelet RNN for the multistep-ahead prediction of multidimensional time series, which was applied to the prediction of satellite telemetry data. Chen et al [30] presented an anomaly detection model based on Bayesian deep learning without domain knowledge that is highly robust to imbalanced satellite telemetry data.…”

Section: Introductionmentioning

confidence: 99%

Accurate Satellite Operation Predictions Using Attention-BiLSTM Model with Telemetry Correlation

Peng,

Jia,

Xie

et al. 2024

Aerospace

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In satellite health management, anomalies are mostly resolved after an event and are rarely predicted in advance. Thus, trend prediction is critical for avoiding satellite faults, which may affect the accuracy and quality of satellite data and even greatly impact safety. However, it is difficult to predict satellite operation using a simple model because satellite systems are complex and telemetry data are copious, coupled, and intermittent. Therefore, this study proposes a model that combines an attention mechanism and bidirectional long short-term memory (attention-BiLSTM) with telemetry correlation to predict satellite behaviour. First, a high-dimensional K-nearest neighbour mutual information method is used to select the related telemetry variables from multiple variables of satellite telemetry data. Next, we propose a new BiLSTM model with an attention mechanism for telemetry prediction. The dataset used in this study was generated and transmitted from the FY3E meteorological satellite power system. The proposed method was compared with other methods using the same dataset used in the experiment to verify its superiority. The results confirmed that the proposed method outperformed the other methods owing to its prediction precision and superior accuracy, indicating its potential for application in intelligent satellite health management systems.

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Section: Introductionmentioning

confidence: 99%

Accurate Satellite Operation Predictions Using Attention-BiLSTM Model with Telemetry Correlation

Peng,

Jia,

Xie

et al. 2024

Aerospace

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“…For example clustering based methods [10] [13] [19] require that the anomalies do not aggregate into clusters; nearest neighbour and density based methods [6] require that the anomalies do not form dense regions in the feature space; spectral methods [7] [3] assume that a projection into a different space exists such that normal and anomalous points can be clearly distinguished. Another approach consists in training a model to predict the signal in the future and then compare the predicted and observed signals to detect anomalies, like in [14]. In this paper we propose the application of the Generative Adversarial Networks (GANs) for the anomaly detection in spacecraft telemetry data.…”

Section: Introductionmentioning

confidence: 99%

A GAN Approach for Anomaly Detection in Spacecraft Telemetries

Ciancarelli

Magistris

Cognetta

et al. 2022

Lecture Notes in Networks and Systems

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In spacecraft health management a large number of time series is acquired and used for on-board units surveillance and for historical data analysis. The early detection of abnormal behaviors in telemetry data can prevent failures in the spacecraft equipment. In this paper we present an advanced monitoring system that was carried out in partnership with Thales Alenia Space Italia S.p.A, a leading industry in the field of spacecraft manufacturing. In particular, we developed an anomaly detection algorithm based on Generative Adversarial Networks, that thanks to their ability to model arbitrary distributions in high dimensional spaces, allow to capture complex anomalies avoiding the burden of hand crafted feature extraction. We applied this method to detect anomalies in telemetry data collected from a simulator of a Low Earth Orbit satellite. One of the strengths of the proposed approach is that it does not require any previous knowledge on the signal. This is particular useful in the context of anomaly detection where we do not have a model of the anomaly. Hence the only assumption we made is that an anomaly is a pattern that lives in a lower probability region of the data space.

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Double internal loop higher-order recurrent neural network-based adaptive control of the nonlinear dynamical system

Kumar

2023

Soft Comput

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Exploiting Wavelet Recurrent Neural Networks for satellite telemetry data modeling, prediction and control

Cited by 12 publications

References 23 publications

Accurate Satellite Operation Predictions Using Attention-BiLSTM Model with Telemetry Correlation

Accurate Satellite Operation Predictions Using Attention-BiLSTM Model with Telemetry Correlation

A GAN Approach for Anomaly Detection in Spacecraft Telemetries

Double internal loop higher-order recurrent neural network-based adaptive control of the nonlinear dynamical system

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