A data-driven method of health monitoring for spacecraft

Xu, Ke; Pi, Dechang

doi:10.1108/aeat-08-2016-0130

Cited by 3 publications

(1 citation statement)

References 32 publications

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“…Ying et al (2018) proposed an anomaly detection algorithm for orbiting satellite remote sensing sequences based on double-window mode, which could detect satellite telemetry sequence anomalies. Kang and Pi (2018) proposed a method called principal component analysis with empirical mode decomposition and sample entropy, which can provide an abnormal warning time before an abnormality occurs.…”

Section: Introductionmentioning

confidence: 99%

A deep auto-encoder satellite anomaly advance warning framework

Chen

Zhao

2021

AEAT

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Purpose The 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/approach This 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. Findings Experiment 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/value This 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.

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Section: Introductionmentioning

confidence: 99%

A deep auto-encoder satellite anomaly advance warning framework

Chen

Zhao

2021

AEAT

Self Cite

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1D-CNN Based Multi-label Aircraft Electrical Signal Classification Method

Wu,

Wan,

Shi

et al. 2024

Lecture Notes in Electrical Engineering

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MRD-Nets: Multi-Scale Residual Networks With Dilated Convolutions for Classification and Clustering Analysis of Spacecraft Electrical Signal

Liu

Zhang

et al. 2019

IEEE Access

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The fault detection of spacecraft electronic load systems is a crucial part of the spacecraft prognostics and health management system. To detect the abnormal state of spacecraft electronic load systems, complex electrical signals should be processed rapidly and accurately. For the fault detection of spacecraft electronic load systems, a robust unsupervised clustering analysis method and an accurate supervised classification method are of great importance. However, the traditional machine learning methods have poor performance when processing high-dimensional signal data because of the lack of ability to extract complex features from the signals. Therefore, neural-network-based deep learning (DL) models which can extract features from signals automatically are more suitable in this situation. In this paper, a novel convolutional neural network (CNN) module, the multi-branch residual module with dilated convolutions (MRD module), is proposed to extract multi-scale features from the electrical signal. Then, a well-designed CNN model named MRD-CNN is presented for the supervised classification task of signal. Furthermore, for the unsupervised clustering task, the clustering variational autoencoder with MRD modules (MRD-CluVAE) is proposed. The MRD-CluVAE can extract high-quality features from signal data and output the clustering results directly. To evaluate the performance of the proposed models, comparisons among the proposed models and other baseline algorithms are carried out. The experimental results show that the MRD-CNN model achieves higher classification performance and stability, while the MRD-CluVAE has a higher clustering accuracy than other algorithms. These methods can be utilized to resolve the classification and recognition problems of spacecraft electronic load signals.

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A data-driven method of health monitoring for spacecraft

Cited by 3 publications

References 32 publications

A deep auto-encoder satellite anomaly advance warning framework

A deep auto-encoder satellite anomaly advance warning framework

1D-CNN Based Multi-label Aircraft Electrical Signal Classification Method

MRD-Nets: Multi-Scale Residual Networks With Dilated Convolutions for Classification and Clustering Analysis of Spacecraft Electrical Signal

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