{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T13:21:50Z","timestamp":1783516910670,"version":"3.55.0"},"reference-count":39,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T00:00:00Z","timestamp":1782259200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ningbo Commonweal Programme","award":["2025S061"],"award-info":[{"award-number":["2025S061"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Early fault detection in microsatellite thermal control systems (TCS) faces fundamental challenges: high-dimensional redundant telemetry channels, overlapping multi-scale periodicities that obscure anomaly signatures, and severely limited daily data downlink (1\u20132 passes per day) that restricts the temporal window for diagnosis. Existing data-driven approaches either rely on supervised learning, requiring labeled fault data that are scarce in practice, or employ univariate analysis that fails to capture inter-sensor spatial correlations. To address these limitations, this paper introduces a hybrid framework integrating information-theoretic feature selection and spatiotemporal deep learning. The Generalized Maximum Information Coefficient (GMIC) quantifies nonlinear dependencies between temperature channels for key channel selection, reducing dimensionality by 82% while preserving diagnostic information. A dual-level Seasonal Trend Decomposition (STL) method disentangles orbital-periodic dynamics from diurnal cycles, effectively isolating distinct thermal characteristics at multiple timescales. Each decomposed component is modeled using Convolutional Neural Network\u2013Long Short-Term Memory (CNN-LSTM) networks to capture spatiotemporal dependencies for accurate temperature prediction. An adaptive threshold-based weighted error fusion mechanism enables early fault detection within a single day of telemetry data. Experimental validation on real satellite telemetry data demonstrates that the proposed framework achieves high-precision fault detection across multiple fault types using a minimal set of temperature channels, significantly outperforming existing benchmarks in both prediction accuracy and detection reliability.<\/jats:p>","DOI":"10.3390\/e28070725","type":"journal-article","created":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T14:27:52Z","timestamp":1783434472000},"page":"725","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Information-Theoretic Channel Selection and Spatiotemporal Deep Learning for Early Fault Detection in Microsatellite Thermal Control Systems"],"prefix":"10.3390","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-7648-4144","authenticated-orcid":false,"given":"Weijian","family":"Pang","sequence":"first","affiliation":[{"name":"Ningbo Institute of Northwestern Polytechnical University, Ningbo 315103, China"},{"name":"Institute of Precision Guidance and Control, School of Astronautics, Northwestern Polytechnical University, Xi\u2019an 710072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Zhou","sequence":"additional","affiliation":[{"name":"Institute of Precision Guidance and Control, School of Astronautics, Northwestern Polytechnical University, Xi\u2019an 710072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingwen","family":"Xu","sequence":"additional","affiliation":[{"name":"Institute of Precision Guidance and Control, School of Astronautics, Northwestern Polytechnical University, Xi\u2019an 710072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinian","family":"Zhi","sequence":"additional","affiliation":[{"name":"Ningbo Institute of Northwestern Polytechnical University, Ningbo 315103, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kulu, E. (2024, January 14\u201318). CubeSats & Nanosatellites-2024 Statistics, Forecast and Reliability. Proceedings of the International Astronautical Congress (IAC), Milan, Italy.","DOI":"10.52202\/078365-0069"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Christensen, C., Dubbert, C., Boensch, N.J., and Mullins, C. (2025, January 21\u201325). Smallsats by the Numbers: Growing Smallsat Activity and Expanding Commercial Capabilities. Proceedings of the AIAA Aviation Forum ASCEND, Las Vegas, NV, USA.","DOI":"10.2514\/6.2025-4109"},{"key":"ref_3","unstructured":"Weston, S.V., Burkhard, C.D., Stupl, J.M., Ticknor, R.L., Yost, B.D., Austin, R.A., Galchenko, P., Newman, L.K., and Soto, L.S. (2026, March 04). State-of-the-Art Small Spacecraft Technology, Available online: https:\/\/ntrs.nasa.gov\/citations\/20250000142."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Sun, P., Jin, G., Li, W.H., and Pang, N. (2019, January 8\u201310). Fault Diagnosis of the Thermal Control System based on Temperature Curve Characteristics. Proceedings of the 5th International Conference on Big Data Information Analytics (BigDIA), Kunming, China.","DOI":"10.1109\/BigDIA.2019.8802651"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"789","DOI":"10.2514\/1.T6495","article-title":"Thermal Analysis of Cooling Channels on Electromechanical Actuator During Actual Flight","volume":"36","author":"Gao","year":"2022","journal-title":"J. Thermophys. Heat Transf."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2679","DOI":"10.1177\/09544100231157132","article-title":"Advanced fault detection and diagnosis in spacecraft attitude control systems: Current state and challenges","volume":"237","author":"Pourtakdoust","year":"2023","journal-title":"Proc. Inst. Mech. Eng. G J. Aerosp. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Hedayati, M., Barzegar, A., and Rahimi, A. (2024). Fault diagnosis and prognosis of satellites and unmanned aerial vehicles: A review. Appl. Sci., 14.","DOI":"10.3390\/app14209487"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"012102","DOI":"10.1088\/1742-6596\/2005\/1\/012102","article-title":"Research on satellites fault diagnosis method based on artificial intelligence","volume":"2005","author":"Song","year":"2021","journal-title":"Proc. J. Phys. Conf. Ser."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.asej.2019.08.006","article-title":"Machine learning techniques for satellite fault diagnosis","volume":"11","author":"Ibrahim","year":"2020","journal-title":"Ain Shams Eng. J."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.1007\/s10462-021-09993-z","article-title":"A novel fault diagnosis method based on CNN and LSTM and its application in fault diagnosis for complex systems","volume":"55","author":"Huang","year":"2022","journal-title":"Artif. Intell. Rev."},{"key":"ref_11","unstructured":"Islam, M.S., and Rahimi, A. (2021, January 7\u20139). Fault prognosis of satellite reaction wheels using a two-step LSTM network. Proceedings of the IEEE International Conference on Prognostics and Health Management (ICPHM), Detroit, MI, USA."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Gong, J., Yang, K., Deng, B., Bao, L., Liao, N., and Jiang, W. (2023, January 22\u201324). Research on space-ground collaborative fault diagnosis technology in satellite based on LSTM. Proceedings of the 6th International Conference on Intelligent Autonomous Systems (ICoIAS), Qinhuangdao, China.","DOI":"10.1109\/ICoIAS61634.2023.00027"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Hedayati, M.S., Barzegar, A., and Rahimi, A. (2024, January 17\u201319). Mitigating Data Scarcity for Satellite Reaction Wheel Fault Diagnosis with Wasserstein Generative Adversarial Networks. Proceedings of the IEEE International Conference on Prognostics and Health Management (ICPHM), Spokane, WA, USA.","DOI":"10.1109\/ICPHM61352.2024.10627589"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"350","DOI":"10.3390\/make4020015","article-title":"An Attention-Based ConvLSTM Autoencoder with Dynamic Thresholding for Unsupervised Anomaly Detection in Multivariate Time Series","volume":"4","author":"Tayeh","year":"2022","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Borr\u00e9, A., Seman, L.O., Camponogara, E., Stefanon, S.F., Mariani, V.C., and Coelho, L.d.S. (2023). Machine Fault Detection Using a Hybrid CNN-LSTM-Attention-Based Model. Sensors, 23.","DOI":"10.3390\/s23094512"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"10354","DOI":"10.1109\/TII.2024.3393002","article-title":"An Interpretable Latent Denoising Diffusion Probabilistic Model for Fault Diagnosis Under Limited Data","volume":"20","author":"Zhang","year":"2024","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., and Zhang, W. (2021, January 19\u201321). Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting. Proceedings of the AAAI Conference on Artificial Intelligenc, Virtual.","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"ref_18","unstructured":"Xu, J., Wu, H., Wang, J., and Long, M. (2022, January 25\u201329). Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy. Proceedings of the International Conference on Learning Representations (ICLR), Virtual."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"108496","DOI":"10.1016\/j.engappai.2024.108496","article-title":"Optimized Single-Image Super-Resolution Reconstruction: A Multimodal Approach Based on Reversible Guidance and Cyclical Knowledge Distillation","volume":"133","author":"Yan","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_20","first-page":"110897","article-title":"A Novel Knowledge Distillation Framework for Bearing Fault Diagnosis Under Imbalanced Samples","volume":"206","author":"Li","year":"2024","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"604","DOI":"10.1016\/j.actaastro.2020.11.004","article-title":"A novel data-driven method for fault detection and isolation of control moment gyroscopes onboard satellites","volume":"180","author":"Muthusamy","year":"2021","journal-title":"Acta Astronaut."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"102405","DOI":"10.1016\/j.aei.2024.102405","article-title":"Hybrid digital twin for satellite temperature field perception and attitude control","volume":"60","author":"Xie","year":"2024","journal-title":"Adv. Eng. Inform."},{"key":"ref_23","first-page":"32369","article-title":"A Graph Neural Network-Based Bearing Fault Detection Method","volume":"13","author":"Li","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_24","unstructured":"Carneiro, M.V., Schaub, H., Lahijanian, M., Lang, K., and Borozdin, K. (2022, January 3\u20137). Machine learning for reaction wheel fault detection using simulated telemetry data. Proceedings of the AIAA SciTech Forum, San Diego, CA, USA."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1007\/s11235-020-00722-5","article-title":"Fault detection in satellite power system using convolutional neural network","volume":"76","author":"Ganesan","year":"2021","journal-title":"Telecommun. Syst."},{"key":"ref_26","unstructured":"Gizzi, E., Owens, H., Pellegrino, N., Trombley, C., Marshall, J., and Sinapov, J. (2022, January 7\u201311). Autonomous System-Level Fault Diagnosis in Satellites Using Housekeeping Telemetry. Proceedings of the Small Satellite Conference, Logan, UT, USA."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Li, J., Rong, X., Meng, H., Ren, P., and Li, J. (2024). Time Series Feature Selection Method Based on Mutual Information. Appl. Sci., 14.","DOI":"10.3390\/app14051960"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Donabedian, M. (2004). Spacecraft Thermal Control Handbook, Volume II: Cryogenics, American Institute of Aeronautics and Astronautics.","DOI":"10.2514\/4.989148"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"124661","DOI":"10.1016\/j.ijheatmasstransfer.2023.124661","article-title":"Numerical investigation of natural circulation loop with supercritical CO2 on the thermal control system of micro spacecrafts","volume":"217","author":"Gao","year":"2023","journal-title":"Int. J. Heat Mass Transf."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"106826","DOI":"10.1016\/j.ast.2021.106826","article-title":"Long-term degradation prediction and assessment with heteroscedasticity telemetry data based on GRU-GARCH and MD hybrid method: An application for satellite","volume":"115","author":"Tao","year":"2021","journal-title":"Aerosp. Sci. Technol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1518","DOI":"10.1126\/science.1205438","article-title":"Detecting Novel Associations in Large Data Sets","volume":"334","author":"Reshef","year":"2011","journal-title":"Science"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1016\/j.renene.2021.04.010","article-title":"A novel hybrid model based on STL decomposition and one-dimensional convolutional neural networks with positional encoding for significant wave height forecast","volume":"173","author":"Yang","year":"2021","journal-title":"Renew. Energy"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1481","DOI":"10.1007\/s11269-022-03419-3","article-title":"NDVI forecasting model based on the combination of time series decomposition and CNN-LSTM","volume":"37","author":"Gao","year":"2023","journal-title":"Water Resour. Manag."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3324","DOI":"10.1007\/s11431-023-2676-6","article-title":"Medium-energy electron spectrometers on Macao Science Satellite-1","volume":"67","author":"Ye","year":"2024","journal-title":"Sci. China Technol. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1283","DOI":"10.1007\/s42405-023-00631-2","article-title":"Thermal interface modeling and experiments with novel multi-layer structure in modular microsatellite","volume":"24","author":"Zhang","year":"2023","journal-title":"Int. J. Aeronaut. Space Sci."},{"key":"ref_36","first-page":"12","article-title":"Current and Future Techniques for Spacecraft Thermal Control. 1. Design Drivers and Current Technologies","volume":"87","year":"2004","journal-title":"ESA Bull."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"109546","DOI":"10.1016\/j.measurement.2021.109546","article-title":"Graph neural network approach for anomaly detection","volume":"180","author":"Xie","year":"2021","journal-title":"Measurement"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"10129","DOI":"10.1109\/TIE.2020.3026272","article-title":"A Deep Learning Based Data-Driven Thruster Fault Diagnosis Approach for Satellite Attitude Control System","volume":"68","author":"Xiao","year":"2021","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"4052","DOI":"10.1109\/TIE.2021.3078349","article-title":"Neural Network Based Finite-Time Attitude Tracking Control of Spacecraft With Angular Velocity Sensor Failures and Actuator Saturation","volume":"69","author":"Ye","year":"2022","journal-title":"IEEE Trans. Ind. Electron."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/28\/7\/725\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T12:56:58Z","timestamp":1783515418000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/28\/7\/725"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,24]]},"references-count":39,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2026,7]]}},"alternative-id":["e28070725"],"URL":"https:\/\/doi.org\/10.3390\/e28070725","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,24]]}}}