A Data-Driven Health Monitoring Method for Satellite Housekeeping Data Based on Probabilistic Clustering and Dimensionality Reduction

LIBSVM: A library for support vector machines

Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918

Estimating the Support of a High-Dimensional Distribution

Schölkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001) · Neural Computation · doi:10.1162/089976601750264965 · Cited by 6,168

Support Vector Data Description

Tax, D. M. J., & Duin, R. P. W. (2003) · Machine Learning · doi:10.1023/b:mach.0000008084.60811.49 · Cited by 3,612

Dynamic bayesian networks: representation, inference and learning

Murphy, K. P., & Russell, S. (2002) · Cited by 2,579

Mixtures of Probabilistic Principal Component Analyzers

Tipping, M. E., & Bishop, C. (1999) · Neural Computation · doi:10.1162/089976699300016728 · Cited by 1,874

A survey on unsupervised outlier detection in high‐dimensional numerical data

Zimek, A., Schubert, E., & Kriegel, H. (2012) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.11161 · Cited by 864 · cited 1×

A model-based approach to reactive self-configuring systems

Williams, B. C., & Nayak, P. P. (1996) · Cited by 487

An approach to spacecraft anomaly detection problem using kernel feature space

Fujimaki, R., Yairi, T., & Machida, K. (2005) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1081870.1081917 · Cited by 225

Unsupervised Anomaly Detection for Liquid-Fueled Rocket Propulsion Health Monitoring

Schwabacher, M., Oza, N. C., & Matthews, B. L. (2009) · Journal of Aerospace Computing Information and Communication · doi:10.2514/1.42783 · Cited by 68

An Anomaly Detection Method for Spacecraft Using Relevance Vector Learning

Fujimaki, R., Yairi, T., & Machida, K. (2005) · Lecture notes in computer science · doi:10.1007/11430919_92 · Cited by 51

DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection

Yang, Y., Zhang, C., Zhou, T., Wen, Q., & Sun, L. (2023) · Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining · doi:10.1145/3580305.3599295 · Cited by 387

Low Earth Orbit Satellite Security and Reliability: Issues, Solutions, and the Road Ahead

Yue, P., An, J., Zhang, J., Ye, J., Pan, G., Wang, S., Xiao, P., & Hanzo, L. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3296160 · Cited by 333

Generalized Isolation Forest for Anomaly Detection

Lesouple, J., Baudoin, C., Spigai, M., & Tourneret, J.-Y. (2021) · Pattern Recognition Letters · doi:10.1016/j.patrec.2021.05.022 · Cited by 202

Artificial intelligence for satellite communication: A review

Fourati, F., & Alouini, M. (2021) · Intelligent and Converged Networks · doi:10.23919/icn.2021.0015 · Cited by 197

Imputation-based Time-Series Anomaly Detection with Conditional Weight-Incremental Diffusion Models

Xiao, C., Gou, Z., Tai, W., Zhang, K., & Zhou, F. (2023) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3580305.3599391 · Cited by 122

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

Tariq, S., Lee, S. Y., Shin, Y., Lee, M. S., Jung, O., Chung, D., & Woo, S. S. (2019) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3292500.3330776 · Cited by 126

Artificial Intelligence for Trusted Autonomous Satellite Operations

Thangavel, K., Sabatini, R., Gardi, A., Ranasinghe, K. C., Hilton, S., Servidia, P. A., & Spiller, D. (2023) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2023.100960 · Cited by 128

Machine Learning Methods for Spacecraft Telemetry Mining

Ibrahim, S. K., Ahmed, A. M., Zeidan, M. A. E., & Ziedan, I. (2018) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2018.2876586 · Cited by 91

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

Hassanien, A. E., Darwish, A., & Abdelghafar, S. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-019-09760-1 · Cited by 84

Machine Learning Techniques for Satellite Fault Diagnosis

Ibrahim, S. K., Ahmed, A. M., Zeidan, M. A. E., & Ziedan, I. (2019) · Ain Shams Engineering Journal · doi:10.1016/j.asej.2019.08.006 · Cited by 79

Anomaly detection in mixed telemetry data using a sparse representation and dictionary learning

Pilastre, B., Boussouf, L., d’Escrivan, S., & Tourneret, J. (2019) · Signal Processing · doi:10.1016/j.sigpro.2019.107320 · Cited by 79

Unsupervised anomaly detection in time-series: An extensive evaluation and analysis of state-of-the-art methods

Mejri, N., Lopez-Fuentes, L., Roy, K., Chernakov, P., Ghorbel, E., & Aouada, D. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124922 · Cited by 94

A Review of Anomaly Detection in Spacecraft Telemetry Data

Fejjari, A., Delavault, A., Camilleri, R., & Valentino, G. (2025) · Applied Sciences · doi:10.3390/app15105653 · Cited by 86

Using Artificial Intelligence for Space Challenges: A Survey

Russo, A., & Lax, G. (2022) · Applied Sciences · doi:10.3390/app12105106 · Cited by 80

A Spacecraft Dataset for Detection, Segmentation and Parts Recognition

Dung, H. A., Chen, B., & Chin, T.-J. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00229 · Cited by 69

Applications and Challenges of Artificial Intelligence in Space Missions

Oche, P. A., Ewa, G. A., & Ibekwe, N. (2024) · IEEE Access · doi:10.1109/access.2021.3132500 · Cited by 68

Explainable anomaly detection in spacecraft telemetry

Cuellar-Carrillo, S., Santos, M., Alonso, F., Fábregas, E., & Farías, G. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108083 · Cited by 76

Anomaly detection of train wheels utilizing short-time Fourier transform and unsupervised learning algorithms

Wan, T. H., Tsang, C. W., Hui, K., & Chung, E. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106037 · Cited by 54

EGNN: Energy-efficient anomaly detection for IoT multivariate time series data using graph neural network

Guo, H., Zhou, Z., Zhao, D., & Gaaloul, W. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.09.028 · Cited by 56

Temporal-Frequency Masked Autoencoders for Time Series Anomaly Detection

Fang, Y., Xie, J., Zhao, Y. L., Lü, C., Gao, Y., & Zheng, K. (2024) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde60146.2024.00099 · Cited by 62

ITAD

Shin, Y., Lee, S., Tariq, S., Lee, M. S., Jung, O., Chung, D., & Woo, S. S. (2020) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3340531.3412716 · Cited by 51

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

He, J., Cheng, Z., & Guo, B. (2023) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2023.109717 · Cited by 47

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

He, J., Cheng, Z., & Guo, B. (2022) · Sensors · doi:10.3390/s22176358 · Cited by 41

Automated Design of Fault Diagnosis CNN Network for Satellite Attitude Control Systems

Zhao, H., Liu, M., Sun, Y., Chen, Z., Duan, G., & Cao, X. (2024) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2024.3384443 · Cited by 40

Shapley Values of Reconstruction Errors of PCA for Explaining Anomaly Detection

Takeishi, N. (2019) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2019.00117 · Cited by 47

Soft decision-making based on decision-theoretic rough set and Takagi-Sugeno fuzzy model with application to the autonomous fault diagnosis of satellite power system

Suo, M., Tao, L., Zhu, B., Chen, Y., Lü, C., & Ding, Y. (2020) · Aerospace Science and Technology · doi:10.1016/j.ast.2020.106108 · Cited by 37

Multi-Objective Hybrid Artificial Intelligence Approach for Fault Diagnosis of Aerospace Systems

Ezzat, D., Hassanien, A. E., Darwish, A. A., Yahia, M., Ahmed, A. M., & Abdelghafar, S. (2021) · IEEE Access · doi:10.1109/access.2021.3064976 · Cited by 36

A Supervised Framework for Recognition of Liquid Rocket Engine Health State Under Steady-State Process Without Fault Samples

Lv, H., Chen, J., Wang, J., Yuan, J., & Liu, Z. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3086900 · Cited by 37

Anomaly detection of satellite telemetry based on optimized extreme learning machine

Abdelghafar, S., Darwish, A. A., Hassanien, A. E., Yahia, M., & Zaghrout, A. A. S. (2019) · Journal of Space Safety Engineering · doi:10.1016/j.jsse.2019.10.005 · Cited by 35

Robustness of AI-based prognostic and systems health management

Khan, S., Tsutsumi, S., Yairi, T., & Nakasuka, S. (2021) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2021.04.001 · Cited by 33

Early lameness detection in dairy cattle based on wearable gait analysis using semi-supervised LSTM-Autoencoder

Zhang, K., Han, S., Wu, J., Cheng, G., Wang, Y., Wu, S., & Liu, J. (2023) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2023.108252 · Cited by 32

SiET: Spatial information enhanced transformer for multivariate time series anomaly detection

Xiong, W., Wang, P., Sun, X., & Wang, J. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111928 · Cited by 32

Unsupervised outlier detection using neural network-based mixtures of probabilistic principal component analyzers for building chiller fault diagnosis

Tra, V., Amayri, M., & Bouguila, N. (2022) · Building and Environment · doi:10.1016/j.buildenv.2022.109620 · Cited by 32

An interpretable graph neural network for real-world satellite power system anomaly detection based on graph filtering

Di, Y., Wang, F., Zhao, Z., Zhai, Z., & Chen, X. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124348 · Cited by 31

Deep Generative Models-Based Anomaly Detection for Spacecraft Control Systems

Ahn, H., Jung, D., & Han-lim Choi, A. (2020) · Sensors · doi:10.3390/s20071991 · Cited by 30

Fault detection in satellite power system using convolutional neural network

Ganesan, M., Lavanya, R. V., & Devi, M. N. (2020) · Telecommunication Systems · doi:10.1007/s11235-020-00722-5 · Cited by 30

A Deep Learning Anomaly Detection Framework for Satellite Telemetry with Fake Anomalies

Wang, Y., Gong, J., Zhang, J., & Han, X. (2022) · International Journal of Aerospace Engineering · doi:10.1155/2022/1676933 · Cited by 30

UAC-AD: Unsupervised Adversarial Contrastive Learning for Anomaly Detection on Multi-Modal Data in Microservice Systems

Liu, H., Huang, X., Jia, M., Jia, T., Han, J., Wu, Z., & Li, Y. (2024) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2024.3411481 · Cited by 28

Deep Clustering-Based Anomaly Detection and Health Monitoring for Satellite Telemetry

Obied, M. A., Ghaleb, F. F. M., Hassanien, A. E., Abdelfattah, A. M. H., & Zakaria, W. (2023) · Big Data and Cognitive Computing · doi:10.3390/bdcc7010039 · Cited by 26

Analyzing the impact of soft errors in VGG networks implemented on GPUs

Wei, J., Ibrahim, Y., Qian, S., Wang, H., Liu, G., Yu, Q., Qian, R., & Shi, J. (2020) · Microelectronics Reliability · doi:10.1016/j.microrel.2020.113648 · Cited by 29

Privatization and Privacy in the Digital City

Allam, Z. (2019) · Springer eBooks · doi:10.1007/978-3-030-29800-5_4 · Cited by 24

Precursor-of-Anomaly Detection for Irregular Time Series

Jhin, S. Y., Lee, J., & Park, N. (2023) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3580305.3599469 · Cited by 24

Satellite Unsupervised Anomaly Detection Based on Deconvolution-Reconstructed Temporal Convolutional Autoencoder

Zhao, H., Liu, M., Qiu, S., & Cao, X. (2023) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2023.3306428 · Cited by 23

Study on the Satellite Telemetry Data Classification Based on Self-Learning

Wan, P., Zhan, Y., & Jiang, W. (2019) · IEEE Access · doi:10.1109/access.2019.2962235 · Cited by 22

Intelligent Health Monitoring Systems for Space Missions Based on Data Mining Techniques

Abdelghafar, S., Darwish, A., & Hassanien, A. E. (2020) · Studies in Computational Intelligence · doi:10.1007/978-3-030-20212-5_4 · Cited by 22

Robust Anomaly Detection for Multivariate Data of Spacecraft Through Recurrent Neural Networks and Extreme Value Theory

Xiang, G., & Lin, R. (2021) · IEEE Access · doi:10.1109/access.2021.3136505 · Cited by 23

A Photovoltaic Model Based Method to Monitor Solar Array Degradation On-Board a Microsatellite

Soon, J. J., Chia, J. W., Aung, H., Lew, J. M., Goh, S. T., & Low, K.-S. (2018) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2018.2822081 · Cited by 20

TSINR: Capturing Temporal Continuity via Implicit Neural Representations for Time Series Anomaly Detection

Li, M., Liu, K., Chen, H., Bu, J., Wang, H., & Wang, H. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3690624.3709266 · Cited by 28

Anomaly Detection in Time Series Data Using Reversible Instance Normalized Anomaly Transformer

Baidya, R., & Jeong, H. (2023) · Sensors · doi:10.3390/s23229272 · Cited by 24

A review of deep learning based anomaly detection

Shi, Y., Yang, G., Wang, X. J., Yu, Q., Feng, Y., & Liu, A. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.132383 · Cited by 24

Application of Artificial Intelligence in Aerospace Engineering and Its Future Directions: A Systematic Quantitative Literature Review

Hassan, K., Thakur, A. K., Singh, G., Singh, J., Gupta, L. R., & Singh, R. (2024) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-024-10105-7 · Cited by 22

Artificial Intelligence Based Mobile Tracking and Antenna Pointing in Satellite-Terrestrial Network

Liu, Q., Yang, J., Zhuang, C., Barnawi, A., & Alzahrani, B. A. (2019) · IEEE Access · doi:10.1109/access.2019.2956544 · Cited by 19

Detecting Anomalies in Small Unmanned Aerial Systems via Graphical Normalizing Flows

Ma, Y., Islam, M. N. A., Cleland‐Huang, J., & Chawla, N. V. (2023) · IEEE Intelligent Systems · doi:10.1109/mis.2023.3252810 · Cited by 18

Multi-level cluster-based satellite-terrestrial integrated communication in Internet of vehicles

Lin, K., Li, C., Pace, P., & Fortino, G. (2019) · Computer Communications · doi:10.1016/j.comcom.2019.10.009 · Cited by 18

Satellite Early Anomaly Detection Using an Advanced Transformer Architecture for Non-Stationary Telemetry Data

Zhao, H., Qiu, S., Yang, J., Guo, J., Liu, M., & Cao, X. (2024) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2024.3357881 · Cited by 17

A Multivariate Anomaly Detector for Satellite Telemetry Data Using Temporal Attention-Based LSTM Autoencoder

Xu, Z., Cheng, Z., & Guo, B. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3296125 · Cited by 16

Satellite Power System State Prediction Based on Online Learning With Parameter Association Rules

Kang, S., Li, Y., Song, Y., Zhou, R., & Pang, J. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3291798 · Cited by 15

Unsupervised Fault Detection for Building Air Handling Unit Systems Using Deep Variational Mixture of Principal Component Analyzers

Tra, V., Amayri, M., & Bouguila, N. (2023) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2023.3331347 · Cited by 14

EST transformer: enhanced spatiotemporal representation learning for time series anomaly detection

Gao, Y., Su, R., Ben, X., & Chen, L. (2025) · Journal of Intelligent Information Systems · doi:10.1007/s10844-025-00918-8 · Cited by 14

Using Machine Learning to Automatically Detect Anomalous Spacecraft Behavior from Telemetry Data

Naik, K. R., Holmgren, A., & Kenworthy, J. (2020) · IEEE Aerospace Conference (AERO) · doi:10.1109/aero47225.2020.9172726 · Cited by 14

Learning the feature distribution similarities for online time series anomaly detection

Fan, J., Yan, G., Zhang, X., Wang, Z., Wu, H., & Wu, J. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106638 · Cited by 14

Performance Evaluation of Machine Learning Methods for Anomaly Detection in CubeSat Solar Panels

Cespedes, A. J. J., Pangestu, B. H. B., Hanazawaᎂᎂ, A., & Cho, M. (2022) · Applied Sciences · doi:10.3390/app12178634 · Cited by 16

Simultaneous fault detection in satellite power systems using deep autoencoders and classifier chain

Ganesan, M., & Lavanya, R. V. (2023) · Telecommunication Systems · doi:10.1007/s11235-023-00998-3 · Cited by 11

Urban Chaos and the AI Messiah

Allam, Z. (2019) · Springer eBooks · doi:10.1007/978-3-030-29800-5_2 · Cited by 11

Predictive Health Status Assessment of a Launch Vehicle Engine in an Ascending Flight Based on Vibration Signals

Zhou, Z., Huang, L., & Lin, R. (2023) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2023.3293457 · Cited by 10

Security in data-driven satellite applications: An overview and new perspectives

Kong, Q., Liu, J., Qu, X., Chen, B., Bao, H., & Xu, L. (2024) · Signal Processing · doi:10.1016/j.sigpro.2024.109755 · Cited by 10

Modern Machine Learning Methods for Telemetry-Based Spacecraft Health Monitoring

Mukhachev, P., Sadretdinov, T., Pritykin, D., Ivanov, A., & Соловьев, С. В. (2021) · Automation and Remote Control · doi:10.1134/s0005117921080014 · Cited by 10

An Adjustable Feature-Weighted Bayesian Model for Hybrid Satellite Telemetry Variables Anomaly Detection Under Multioperating Conditions

Liu, M., Luo, T., Zhang, L., Cao, X., & Duan, G. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3325874 · Cited by 10

Multivariate Time-series Classification for Automated Fault Detection in Satellite Power Systems

Dheepadharshani, S., Anandh, S., Bhavinaya, K. B., & Lavanya, R. V. (2019) · doi:10.1109/iccsp.2019.8698017 · Cited by 10

Fault Warning of Satellite Momentum Wheels with a Lightweight Transformer Improved by FastDTW

Gao, Y., Qiu, S., Liu, M., Zhang, L., & Cao, X. (2025) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2024.124689 · Cited by 9

Mixer-transformer: Adaptive anomaly detection with multivariate time series

Fang, X., Chen, Y., Bhuiyan, M. Z. A., He, X., Bian, G., Crespi, N., & Jing, X. (2025) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2025.104216 · Cited by 9

A Data-Driven Approach to Anomaly Detection and Health Monitoring for Artificial Satellites

Yairi, T., Fukushima, Y., Liew, C. F., Sakai, Y., & Yamaguchi, Y. (2021) · Lecture notes in mechanical engineering · doi:10.1007/978-981-15-9199-0_13 · Cited by 9

A Multilayered and Multifactorial Health Assessment Method for Launch Vehicle Engine under Vibration Conditions

Lin, R., Huang, L., Liu, Z., Zhou, X., & Zhou, Z. (2023) · Aerospace · doi:10.3390/aerospace10060505 · Cited by 9

GalaxAI: Machine learning toolbox for interpretable analysis of spacecraft telemetry data

Kostovska, A., Petković, M., Stepišnik, T., Lucas, L., Finn, T. J., Heras, J. A. M., Panov, P., Džeroski, S., Donati, A., Simidjievski, N., et al. (2021) · IEEE International Conference on Space Mission Challenges for Information Technology · doi:10.1109/smc-it51442.2021.00013 · Cited by 9

Anomaly Detection for Agile Satellite Attitude Control System Using Hybrid Deep-Learning Technique

Mohamed, M. I., Badran, K. M., & Hussien, A. E. (2023) · Journal of Aerospace Information Systems · doi:10.2514/1.i011280 · Cited by 9

TF-GAN: Satellite Anomaly Detection via Generative Adversarial Networks and Time–Frequency Spectrum

Jiao, J., Li, G., Wang, J., Zhao, Z., Li, J., & Chen, H. (2024) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2024.3440280 · Cited by 8

Anomaly Detection Using Deep Learning Respecting the Resources on Board a CubeSat

Horne, R., Mauw, S., Mizera, A., Stemper, A., & Thoemel, J. (2023) · Journal of Aerospace Information Systems · doi:10.2514/1.i011232 · Cited by 8

Triple Attention: An Integrated Approach for Interpretable Anomaly Detection in Temporal and Association Dimensions

Yu, B., Yu, Y., Xiang, G., & Lin, R. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3460930 · Cited by 7

Toward an Onboard Anomaly Detection and Identification Method for Satellites

Katsube, S., & Sahara, H. (2025) · IEEE Access · doi:10.1109/access.2025.3593489 · Cited by 7

A Masked Attention Network with Query Sparsity Measurement for Time Series Anomaly Detection

Zhong, J., Zuo, E., Chen, C., Chen, C., Yan, J., Li, T., & Lv, X. (2023) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme55011.2023.00466 · Cited by 7

State-of-Health Prediction for Reaction Wheel of On-Orbit Satellite Based on Fourier Broad Learning System

Zhang, X., Wang, Z., Ma, J., Li, S., & Suo, M. (2021) · IEEE Access · doi:10.1109/access.2021.3111592 · Cited by 7

A Survey on Anomaly Detection and Diagnosis Problem in the Space System Operation

Taburoğlu, S. (2019) · Journal of Intelligent Systems Theory and Applications · doi:10.38016/jista.509532 · Cited by 7

OXI: An online tool for visualization and annotation of satellite time series data

Ruszczak, B., Kotowski, K., Andrzejewski, J., Haskamp, C., & Nalepa, J. (2023) · SoftwareX · doi:10.1016/j.softx.2023.101476 · Cited by 7

TCAD: Unsupervised Anomaly Detection Based on Global Local Representation Differences

Duan, Y., Xiang, M., Bingtao, Z., Fu, D., & Liu, H. (2022) · IEEE Access · doi:10.1109/access.2022.3216930 · Cited by 6

Hypergraph Learning based Recommender System for Anomaly Detection, Control and Optimization

Srinivas, S. S., Sarkar, R. K., & Runkana, V. (2022) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata55660.2022.10020888 · Cited by 6

SRdetector: Sequence Reconstruction Method for Microservice Anomaly Detection

Ge, H., Ji, X., Peng, F., Chen, R., Xiang, N., Zhang, K., & Wu, W. (2024) · Electronics · doi:10.3390/electronics14010065 · Cited by 7

Anomaly detection of satellite telemetry data based on extended dominant sets clustering

Jin, X., Wang, H. Q., & Jin, Z. H. (2023) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2489/1/012036 · Cited by 6

A Spacecraft Dataset for Detection, Segmentation and Parts RecognitionPreprint

Hoang, D. A., Chen, B., & Chin, T.-J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.08186 · Cited by 6

A responsive approach to multivariate time-series anomaly detection with K-distance based calibrated reconstruction

Fan, J., Bi, Y., Yao, J., Huang, L., Wu, H., & Wu, J. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.131689 · Cited by 5

Prompt Engineering Approach Study for Supervised Fine-Tuned (SFT) Large Language Models (LLMs) in Spacecraft Fault Diagnosis

Xia, Q. L., Zhao, H., & Liu, M. (2024) · Conference on Fully Actuated System Theory and Applications (FASTA) · doi:10.1109/fasta61401.2024.10595331 · Cited by 5

Diffusion Model in Normal Gathering Latent Space for Time Series Anomaly Detection

Han, J., Feng, S., Zhou, M. Q., Zhang, X., Ong, Y.-S., & Li, X. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-70352-2_17 · Cited by 4

CSTSINR: improving temporal continuity via convolutional structured implicit neural representations for time series anomaly detection

Liu, K., Li, M., Bu, J., Wang, H., & Wang, H. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.108129 · Cited by 4

Autoencoder-K-Means Algorithm for Efficient Anomaly Detection to Improve Space Operations

Akbarian, H., Mahgoub, I., & Williams, A. (2024) · International Conference on Smart Applications, Communications and Networking (SmartNets) · doi:10.1109/smartnets61466.2024.10577704 · Cited by 4

Designing ethical artifacts has resulted in creative design

Sekiguchi, K., & Hori, K. (2020) · AI & Society · doi:10.1007/s00146-020-01043-6 · Cited by 4

Mining diagnostic knowledge from spacecraft data based on Spark cluster

Wang, H., Yu, J., Tang, D., Han, D., Tian, L., & Dai, J. (2020) · IEEE Conference on Industrial Electronics and Applications (ICIEA) · doi:10.1109/iciea48937.2020.9248423 · Cited by 4

Contrastive Learning with Prototype-Based Negative Mixing for Satellite Telemetry Anomaly Detection

Guohang, G. U. O., Hu, T., Zhou, T., Li, H., & Liu, Y. (2023) · Sensors · doi:10.3390/s23104723 · Cited by 4

Anomaly Detection in Time Series Satellite Data Using a Deep Learning Method

Han, X., Wang, Y., Zhang, X., & Xu, N. (2023) · CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS) · doi:10.1109/safeprocess58597.2023.10295895 · Cited by 4

Anomaly Detection in Mixed Time-Series Using A Convolutional Sparse Representation With Application To Spacecraft Health Monitoring

Pilastre, B., Silva, G., Boussouf, L., d’Escrivan, S., Rodríguez, P., & Tourneret, J. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9053929 · Cited by 4

Explainable symptom detection in telemetry of ISS with Random Forest and SpecTRM

Iino, S., Nomoto, H., Michiura, Y., Hirose, T., Sasaki, M., Ishizawa, S., Fukui, T., Ishitsuka, Y., Itabashi, Y., Shibayama, H., et al. (2022) · IEEE Aerospace Conference (AERO) · doi:10.1109/aero53065.2022.9843739 · Cited by 4

Explainable Shape Anomaly Detection of Space Targets from ISAR Image Sequences

Wang, Z., Duan, J., & Zhang, L. (2025) · Remote Sensing · doi:10.3390/rs17213541 · Cited by 3

A Data-Driven Approach to Anomaly Detection and Health Monitoring for Artificial Satellites

Yairi, T., Fukushima, Y., Liew, C. F., Sakai, Y., & Yamaguchi, Y. (2021) · Lecture notes in mechanical engineering · doi:10.1007/978-981-15-9199-0_13 · Cited by 9

Spacecraft Telemetry Monitoring Method Based on Dimensionality Reduction and Clustering

Yairi, T., Inui, M., Kawahara, Y., & Takata, N. (2011) · JOURNAL OF THE JAPAN SOCIETY FOR AERONAUTICAL AND SPACE SCIENCES · doi:10.2322/jjsass.59.197 · Cited by 10

Deep Clustering-Based Anomaly Detection and Health Monitoring for Satellite Telemetry

Obied, M. A., Ghaleb, F. F. M., Hassanien, A. E., Abdelfattah, A. M. H., & Zakaria, W. (2023) · Big Data and Cognitive Computing · doi:10.3390/bdcc7010039 · Cited by 26

SDS-4運用における学習型テレメトリ監視システムの性能向上検討(1): 手法と実装

矢入, 健., 直也, 武., 康佑, 秋., 尚樹, 西., 佑太, 中., 昇, 高., Yairi, T., Takeishi, N., Akimoto, K., Nishimura, N., et al. (2015) · Institutional Repositories DataBase (IRDB)

Health Index for Satellite System Based on Characteristics of Telemetry Data

Katsube, S., & Sahara, H. (2023) · PHM Society Asia-Pacific Conference · doi:10.36001/phmap.2023.v4i1.3655