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 · see citing works

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 · see citing works

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 3,553 · see citing works

Dynamic bayesian networks: representation, inference and learning

Murphy, K. P., & Russell, S. (2002) · Cited by 2,563 · see citing works

Mixtures of Probabilistic Principal Component Analyzers

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

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× · see citing works

A model-based approach to reactive self-configuring systems

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

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 · see citing works

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 · see citing works

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 · see citing works

General Purpose Data-Driven Monitoring for Space Operations

Iverson, D. L., Martin, R. A., Schwabacher, M., Spirkovska, L., Taylor, W., Mackey, R., Castle, J. P., & Baskaran, V. (2012) · Journal of Aerospace Computing Information and Communication · doi:10.2514/1.54964 · Cited by 50 · see citing works

Principal Component Analysis and Effective K-means Clustering

Ding, C. H. Q., & He, X. (2004) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611972740.54 · Cited by 36 · see citing works

Efficient On-line Fault Diagnosis for Non-Linear Systems

Hutter, F., & Dearden, R. (2003) · Cited by 31 · see citing works

Applying Model-Based Reasoning to the FDIR of the Command and Data Handling Subsystem of the International Space Station

Robinson, P., Shirley, M., Fletcher, D. P., Alena, R., Duncavage, D. P., & Lee, C. (2003) · Cited by 22 · see citing works

Adaptive limit checking for spacecraft telemetry data using regression tree learning

Yairi, T., Nakatsugaawa, M., Ishihama, N., Hori, K., Nakasuka, S., & Machida, K. (2005) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.2004.1401008 · Cited by 21 · see citing works

Intelligent fault isolation and diagnosis for communication satellite systems

Tallo, D., Durkin, J., & Petrik, E. J. (1992) · Telematics and Informatics · doi:10.1016/s0736-5853(05)80035-8 · Cited by 16 · see citing works

Small Demonstration Satellite-4 (SDS-4): Development, Flight Results, and Lessons Learned in JAXA’s Microsatellite Project

Nakamura, Y., Nishijo, K., Murakami, N., Kawashima, K., Horikawa, Y., Yamamoto, K., Ohtani, T., Takhashi, Y., & Inoue, K. (2013) · Digital Commons - USU (Utah State University) · Cited by 15 · see citing works

Diagnosis Method for Spacecraft Using Dynamic Bayesian Networks

Kawahara, Y., Yairi, T., & Machida, K. (2005) · Cited by 12 · see citing works

FULLY AUTOMATIC AND OPERATOR-LESS ANOMALY DETECTING GROUND SUPPORT SYSTEM FOR MARS PROBE "NOZOMI"

Nishigori, N., Limited, F., Hashimoto, M., Choki, A., & Mizutani, M. (2001) · Cited by 10 · see citing works

Event diagnosis and recovery in real-time on-board autonomous mission control

Ciceri, F., & Marradi, L. (1994) · Lecture notes in computer science · doi:10.1007/3-540-58822-1_107 · Cited by 10 · see citing works

Adaptive Limit Checking for Spacecraft Telemetry Data Using Kernel Principal Component Analysis

Inui, M., Kawahara, Y., Goto, K., Yairi, T., & Machida, K. (2009) · TRANSACTIONS OF THE JAPAN SOCIETY FOR AERONAUTICAL AND SPACE SCIENCES SPACE TECHNOLOGY JAPAN · doi:10.2322/tstj.7.pf_11 · Cited by 7 · see citing works

Fuzzy Inductive Reasoning and Possibilistic Logic for Space Systems Failure Smart Detection and Identification

Finzi, A. E., Lavagna, M. R., & Sangiovanni, G. (2003) · Virtual Community of Pathological Anatomy (University of Castilla La Mancha) · Cited by 7 · see citing works

An Advanced Model-Based Diagnosis Engine

Fijany, A., Vatan, F., Barrett, A., James, M., Mackey, R., Williams, C., & Dvorak, D. L. (2003) · Cited by 6 · see citing works

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 · see citing works

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 306 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 118 · see citing works

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 112 · see citing works

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 89 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 78 · see citing works

Using Artificial Intelligence for Space Challenges: A Survey

Russo, A., & Lax, G. (2022) · Applied Sciences · doi:10.3390/app12105106 · Cited by 77 · see citing works

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 · see citing works

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 · see citing works

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 63 · see citing works

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 · see citing works

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 53 · see citing works

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 56 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 39 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 25 · see citing works

Privatization and Privacy in the Digital City

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

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 23 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 21 · see citing works

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 · see citing works

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 20 · see citing works

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

Baidya, R., & Jeong, H. (2023) · Sensors · doi:10.3390/s23229272 · Cited by 20 · see citing works

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 20 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 13 · see citing works

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 13 · see citing works

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 · see citing works

Urban Chaos and the AI Messiah

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

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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