{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T16:30:54Z","timestamp":1776529854468,"version":"3.51.2"},"reference-count":32,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2019,7,22]],"date-time":"2019-07-22T00:00:00Z","timestamp":1563753600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Satellite telemetry data contains satellite status information, and ground-monitoring personnel need to promptly detect satellite anomalies from these data. This paper takes the satellite power subsystem as an example and presents a reliable anomaly detection method. Due to the lack of abnormal data, the autoencoder is a powerful method for unsupervised anomaly detection. This study proposes a novel stage-training denoising autoencoder (ST-DAE) that trains the features, in stages. This novel method has better reconstruction capabilities in comparison to common autoencoders, sparse autoencoders, and denoising autoencoders. Meanwhile, a cluster-based anomaly threshold determination method is proposed. In this study, specific methods were designed to evaluate the autoencoder performance in three perspectives. Experiments were carried out on real satellite telemetry data, and the results showed that the proposed ST-DAE generally outperformed the autoencoders, in comparison.<\/jats:p>","DOI":"10.3390\/s19143216","type":"journal-article","created":{"date-parts":[[2019,7,22]],"date-time":"2019-07-22T02:55:37Z","timestamp":1563764137000},"page":"3216","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Detecting Anomalies of Satellite Power Subsystem via Stage-Training Denoising Autoencoders"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8720-9274","authenticated-orcid":false,"given":"Weihua","family":"Jin","sequence":"first","affiliation":[{"name":"Research Center of Satellite Technology, Harbin Institute of Technology, Harbin 150080, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Sun","sequence":"additional","affiliation":[{"name":"Beijing Institute of Spacecraft System Engineering, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhidong","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing Institute of Spacecraft System Engineering, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shijie","family":"Zhang","sequence":"additional","affiliation":[{"name":"Research Center of Satellite Technology, Harbin Institute of Technology, Harbin 150080, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhonggui","family":"Chen","sequence":"additional","affiliation":[{"name":"Beijing Institute of Spacecraft System Engineering, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,22]]},"reference":[{"key":"ref_1","unstructured":"Ziqian, C. (2007). Research on Satellite Power System Diagnosis Based on Qualitative Model [D]. [Ph.D. Thesis, Harbin Institute of Technology]."},{"key":"ref_2","unstructured":"Assendorp, J.P. (2017). Deep Learning for Anomaly Detection in Multivariate Time Series Data. [Ph.D. Thesis, Hochschulinformations-und Bibliotheksservice HIBS der HAW Hamburg]."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2082","DOI":"10.1016\/j.microrel.2015.07.010","article-title":"Anomaly detection for satellite power subsystem with associated rules based on Kernel Principal Component Analysis","volume":"55","author":"Pan","year":"2015","journal-title":"Microelectron. Reliab."},{"key":"ref_4","unstructured":"Zong, B., Song, Q., Martin, R.M., Wei, C., Lumezanu, C., Cho, D., and Haifeng, C. (May, January 30). Deep autoencoding gaussian mixture model for unsupervised anomaly detection. Proceedings of the 6th International conference on Learning Repretations, Vancouver, BC, Canada."},{"key":"ref_5","first-page":"1","article-title":"Anomaly Detection as a Service: Challenges, Advances, and Opportunities. Anomaly Detection as a Service: Challenges, Advances, and Opportunities","volume":"9","author":"Danfeng","year":"2017","journal-title":"Synth. Lect. Inf. Secur. Priv. Trust"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.patcog.2016.03.028","article-title":"High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning","volume":"58","author":"Erfani","year":"2016","journal-title":"Pattern Recognit."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.jnca.2015.11.016","article-title":"A survey of network anomaly detection techniques","volume":"60","author":"Ahmed","year":"2016","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1541880.1541882","article-title":"Anomaly detection:A survey","volume":"41","author":"Chandola","year":"2009","journal-title":"ACM Comput. Surv."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"7698","DOI":"10.1016\/j.eswa.2010.12.141","article-title":"Incremental SVM based on reserved set for network intrusion detection","volume":"38","author":"Yang","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_10","unstructured":"Sch\u00f6lkopf, B., Williamson, R.C., and Smola, A.J. (December, January 29). Support Vector Method for Novelty Detection. Proceedings of the International Conference on Neural Information Processing Systems, Denver, CO, USA."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.compeleceng.2018.11.003","article-title":"A data-driven metric learning-based scheme for unsupervised network anomaly detection","volume":"73","author":"Aliakbarisani","year":"2019","journal-title":"Comput. Electr. Eng."},{"key":"ref_12","unstructured":"Bo, L., and Wang, X. (2010, January 8\u201310). Fault detection and reconstruction for micro-satellite power subsystem based on PCA. Proceedings of the International Symposium on Systems & Control in Aeronautics & Astronautics, Harbin, China."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.procs.2016.08.115","article-title":"The Entropy and PCA Based Anomaly Prediction in Data Streams","volume":"96","author":"Hong","year":"2016","journal-title":"Procedia Comput. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Olukanmi, O.P., and Twala, B. (December, January 30). Sensitivity analysis of an outlier-aware k-means clustering algorithm. Proceedings of the 2017 Pattern Recognition Association of South Africa and Robotics and Mechatronics (PRASA-RobMech), Bloemfontein, South Africa.","DOI":"10.1109\/RoboMech.2017.8261125"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.asoc.2017.06.035","article-title":"Multivariate time series anomaly detection: A framework of Hidden Markov Models","volume":"60","author":"Li","year":"2017","journal-title":"Appl. Soft Comput."},{"key":"ref_16","unstructured":"Malhotra, P., Ramakrishnan, A., Anand, G., Vig, L., and Agarwal, P. (2016). LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection. arXiv."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Taylor, A., Leblanc, S., and Japkowicz, N. (2016, January 17\u201319). Anomaly Detection in Automobile Control Network Data with Long Short-Term Memory Networks. Proceedings of the 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA), Montreal, QC, Canada.","DOI":"10.1109\/DSAA.2016.20"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1016\/j.apenergy.2017.12.005","article-title":"Analytical investigation of autoencoder-based methods for unsupervised anomaly detection in building energy data","volume":"211","author":"Fan","year":"2018","journal-title":"Appl. Energy"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Rumelhart, E.D., Hinton, G.E., and Williams, R.J. (1988). Learning Internal Representations by Error Propagation, MIT Press.","DOI":"10.1016\/B978-1-4832-1446-7.50035-2"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"Lecun","year":"2015","journal-title":"Nature"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Vincent, P., Larochelle, H., and Bengio, Y. (2008, January 5\u20139). Extracting and composing robust features with denoising autoencoders. Proceedings of the International Conference on Machine Learning, Helsinki, Finland.","DOI":"10.1145\/1390156.1390294"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3311","DOI":"10.1016\/S0042-6989(97)00169-7","article-title":"Sparse coding with an overcomplete basis set: A strategy employed by V1?","volume":"37","author":"Olshausen","year":"1997","journal-title":"Vis. Res."},{"key":"ref_23","first-page":"3371","article-title":"Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion","volume":"11","author":"Vincent","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref_24","first-page":"1","article-title":"Sparse autoencoder","volume":"72","author":"Ng","year":"2011","journal-title":"CS294A Lect. Notes"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"402","DOI":"10.4097\/kjae.2013.64.5.402","article-title":"The prevention and handling of the missing data","volume":"64","author":"Hyun","year":"2013","journal-title":"Korean J. Anesth."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Laberge, Y. (2011). Advising on Research Methods: A Consultant\u2019s Companion, Johannes van Kessel Publishing.","DOI":"10.1080\/02664763.2011.559375"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1177\/004912417700600206","article-title":"The Treatment of Missing Data in Multivariate Analysis","volume":"6","author":"Kim","year":"1977","journal-title":"Sociol. Methods Res."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1355","DOI":"10.1056\/NEJMsr1203730","article-title":"The prevention and treatment of missing data in clinical trials","volume":"367","author":"Little","year":"2012","journal-title":"N. Engl. J. Med."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Idreos, S., Papaemmanouil, O., and Chaudhuri, S. (June, January 31). Overview of Data Exploration Techniques. Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, Victoria, Australia.","DOI":"10.1145\/2723372.2731084"},{"key":"ref_30","unstructured":"Kuchaiev, O., and Ginsburg, B. (2017). Training Deep AutoEncoders for Collaborative Filtering. arXiv."},{"key":"ref_31","unstructured":"MacQueen, J. (July, January 21). Some methods for classification and analysis of multivariate observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Oakland, CA, USA."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Kim, C.A., Park, W.H., and Dong, H.L. (2013). A Framework for Anomaly Pattern Recognition in Electronic Financial Transaction Using Moving Average Method, Springer.","DOI":"10.1007\/978-94-007-5860-5_12"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/14\/3216\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:08:09Z","timestamp":1760188089000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/14\/3216"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,22]]},"references-count":32,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2019,7]]}},"alternative-id":["s19143216"],"URL":"https:\/\/doi.org\/10.3390\/s19143216","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,7,22]]}}}