{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T02:45:36Z","timestamp":1768445136595,"version":"3.49.0"},"reference-count":48,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,2,25]],"date-time":"2022-02-25T00:00:00Z","timestamp":1645747200000},"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>The satellite power subsystem is responsible for all power supply in a satellite, and is an important component of it. The system\u2019s performance has a direct impact on the operations of other systems as well as the satellite\u2019s lifespan. Sequence to sequence (seq2seq) learning has recently advanced, gaining even more power in evaluating complicated and large-scale data. The potential of the seq2seq model in detecting anomalies in the satellite power subsystem is investigated in this work. A seq2seq-based scheme is given, with a thorough comparison of different neural-network cell types and levels of data smoothness. Three specific approaches were created to evaluate the seq2seq model performance, taking into account the unsupervised learning mechanism. The findings reveal that a CNN-based seq2seq with attention model under suitable data-smoothing conditions has a better ability to detect anomalies in the satellite power subsystem.<\/jats:p>","DOI":"10.3390\/s22051819","type":"journal-article","created":{"date-parts":[[2022,2,27]],"date-time":"2022-02-27T20:48:33Z","timestamp":1645994913000},"page":"1819","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["An Analytical Investigation of Anomaly Detection Methods Based on Sequence to Sequence Model in Satellite Power Subsystem"],"prefix":"10.3390","volume":"22","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":"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":"Bo","family":"Sun","sequence":"additional","affiliation":[{"name":"Beijing Institute of Spacecraft System Engineering, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengli","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Materials Science & Engineering, Harbin Institute of Technology, Harbin 150080, 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"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Jin, W., Sun, B., Li, Z., Zhang, S., and Chen, Z. (2019). Detecting anomalies of satellite power subsystem via stage-training denoising autoencoders. Sensors, 19.","DOI":"10.3390\/s19143216"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"106108","DOI":"10.1016\/j.ast.2020.106108","article-title":"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","volume":"106","author":"Suo","year":"2020","journal-title":"Aerosp. Sci. Technol."},{"key":"ref_3","unstructured":"Andrienko, N. (2006). Exploratory Analysis of Spatial and Temporal Data, Springer."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Azevedo, D.R., Ambr\u00f3sio, A.M., and Vieira, M. (2012, January 8\u201311). Applying data mining for detecting anomalies in satellites. Proceedings of the 9th European Dependable Computing Conference, Sibiu, Romania.","DOI":"10.1109\/EDCC.2012.19"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2250","DOI":"10.1109\/TKDE.2013.184","article-title":"Outlier Detection for Temporal Data: A Survey","volume":"26","author":"Gupta","year":"2014","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_6","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. Eng."},{"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":"15","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","first-page":"582","article-title":"Support vector method for novelty detection","volume":"12","author":"Williamson","year":"1999","journal-title":"NIPS"},{"key":"ref_10","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_11","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":"Yi","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.jpowsour.2014.07.176","article-title":"Lithium-ion battery state of health monitoring and remaining useful life prediction based on support vector regression-particle filter","volume":"271","author":"Dong","year":"2014","journal-title":"J. Power Source"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/j.apenergy.2015.08.119","article-title":"A novel multistage Support Vector Machine based approach for Li ion battery remaining useful life estimation","volume":"159","author":"Patil","year":"2015","journal-title":"Appl. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1092","DOI":"10.1016\/j.ast.2018.11.049","article-title":"Data-driven fault diagnosis of satellite power system using fuzzy Bayes risk and SVM","volume":"84","author":"Suo","year":"2019","journal-title":"Aerosp. Sci. Technol."},{"key":"ref_16","unstructured":"Lee, B., and Wang, X. (2010, January 8\u201310). Fault detection and reconstruction for micro-satellite power subsystem based on PCA. Proceedings of the 3rd International Symposium on Systems and Control in Aeronautics and Astronautics, Harbin, China."},{"key":"ref_17","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_18","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_19","doi-asserted-by":"crossref","unstructured":"Olukanmi, P.O., 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_20","doi-asserted-by":"crossref","unstructured":"Gertler, J.J. (2017). Fault Detection and Diagnosis in Engineering Systems, CRC Press.","DOI":"10.1201\/9780203756126"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Syarif, I., Prugel-Bennett, A., and Wills, G. (2012). Unsupervised clustering approach for network anomaly detection. Proceedings of the International Conference on Networked Digital Technologies, Springer.","DOI":"10.1007\/978-3-642-30507-8_13"},{"key":"ref_22","unstructured":"Gao, B., Ma, H.Y., and Yang, Y.H. (2002, January 4\u20135). HMMs (Hidden Markov models) based on anomaly intrusion detection method. Proceedings of the International Conference on Machine Learning & Cybernetics, Beijing, China."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1145\/604264.604269","article-title":"Detection and classification of intrusions and faults using sequences of system calls","volume":"30","author":"Cabrera","year":"2001","journal-title":"ACM SIGMOD Rec."},{"key":"ref_24","unstructured":"Endler, D. (1998, January 7\u201311). Intrusion detection. Applying machine learning to Solaris audit data. Proceedings of the 14th Annual Computer Security Applications Conference, Phoenix, AZ, USA."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1145\/322510.322526","article-title":"An application of machine learning to anomaly detection","volume":"2","author":"Lane","year":"1997","journal-title":"Trans. Inf. Forensics Secur."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1145\/322510.322526","article-title":"Temporal sequence learning and data reduction for anomaly detection","volume":"2","author":"Lane","year":"1999","journal-title":"ACM Trans. Inf. Syst. Secur. (TISSEC)"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10115-006-0034-6","article-title":"Finding the most unusual time series subsequence: Algorithms and applications","volume":"11","author":"Keogh","year":"2007","journal-title":"Knowl. Inf. Syst."},{"key":"ref_28","first-page":"147","article-title":"A learning algorithm for Boltzmann machines","volume":"9","author":"Ackley","year":"1985","journal-title":"Cognit. Sci."},{"key":"ref_29","unstructured":"Assendorp, J.P. (2017). Deep Learning for Anomaly Detection in Multivariate Time Series Data. [Ph.D. Thesis, Hochschule f\u00fcr Angewandte Wissenschaften Hamburg]."},{"key":"ref_30","unstructured":"Baldi, P. (July, January 26). Autoencoders, unsupervised learning, and deep architectures. Proceedings of the of ICML Workshop on Unsupervised and Transfer Learning, JMLR Workshop and Conference Proceedings, Edinburgh, UK."},{"key":"ref_31","unstructured":"Malhotra, P., Ramakrishnan, A., and Anand, G. (2016). LSTM-based encoder-decoder for multi-sensor anomaly detection. arXiv."},{"key":"ref_32","unstructured":"Sutskever, I., Vinyals, O., and Le, Q.V. (2014, January 8\u201313). Sequence to Sequence Learning with Neural Networks. Proceedings of the Advances in Neural Information Processing Systems (NIPS), Montreal, QC, Canada."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Mousavi, S., and Afghah, F. (2019, January 12\u201317). Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: A sequence to sequence deep learning approach. Proceedings of the ICASSP 2019\u20142019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK.","DOI":"10.1109\/ICASSP.2019.8683140"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Jiang, K., Liang, S., and Meng, L. (2020, January 6\u201319). A Two-level attention-based sequence-to-sequence model for accurate inter-patient arrhythmia detection. Proceedings of the 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Seoul, Korea.","DOI":"10.1109\/BIBM49941.2020.9313453"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Cho, K., Van Merri\u00ebnboer, B., and Gulcehre, C. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv.","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref_36","unstructured":"Chung, J., Gulcehre, C., and Cho, K.H. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv."},{"key":"ref_37","unstructured":"Bahdanau, D., Cho, K., and Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv."},{"key":"ref_38","first-page":"2204","article-title":"Recurrent models of visual attention","volume":"27","author":"Mnih","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_39","unstructured":"Vaswani, A., Shazeer, N., and Parmar, N. (2017). Attention is all you need. arXiv."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Luong, M.T., Pham, H., and Manning, C.D. (2015). Effective approaches to attention-based neural machine translation. arXiv.","DOI":"10.18653\/v1\/D15-1166"},{"key":"ref_41","unstructured":"Gehring, J., Auli, M., and Grangier, D. (2017, January 6\u201311). Convolutional sequence to sequence learning. Proceedings of the International Conference on Machine Learning, Sydney, NSW, Australia."},{"key":"ref_42","first-page":"1","article-title":"Evaluation of 1D CNN autoencoders for lithium-ion battery condition assessment using synthetic data","volume":"11","author":"Valant","year":"2019","journal-title":"Proc. Annu. Conf. PHM Soc."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.ymssp.2017.03.034","article-title":"A novel deep autoencoder feature learning method for rotating machinery fault diagnosis","volume":"95","author":"Shao","year":"2017","journal-title":"Mech. Syst. Signal."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Su, Y., Zhao, Y., and Sun, M. (2021). Detecting outlier machine instances through gaussian mixture variational autoencoder with one dimensional CNN. IEEE Trans. Comput.","DOI":"10.1109\/TC.2021.3065073"},{"key":"ref_45","unstructured":"Smith, S. (2013). Digital Signal Processing: A Practical Guide for Engineers and Scientist, Elsevier."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1080\/01621459.1983.10478008","article-title":"A method for comparing two hierarchical clusterings","volume":"78","author":"Fowlkes","year":"1983","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/03610927408827101","article-title":"A dendrite method for cluster analysis","volume":"3","author":"Harabasz","year":"1974","journal-title":"Commun. Stat. Theory Methods"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","article-title":"Silhouettes: A graphical aid to the interpretation and validation of cluster analysis","volume":"20","author":"Rousseeuw","year":"1987","journal-title":"J. Comput. Appl. Math."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/5\/1819\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:27:21Z","timestamp":1760135241000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/5\/1819"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,25]]},"references-count":48,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["s22051819"],"URL":"https:\/\/doi.org\/10.3390\/s22051819","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,25]]}}}