{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:35:08Z","timestamp":1760142908140,"version":"build-2065373602"},"reference-count":36,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T00:00:00Z","timestamp":1704844800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2023YFB3307102","U22B6001"],"award-info":[{"award-number":["2023YFB3307102","U22B6001"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation Integration Project","doi-asserted-by":"publisher","award":["2023YFB3307102","U22B6001"],"award-info":[{"award-number":["2023YFB3307102","U22B6001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The power system, as a core component of a launch vehicle, has a crucial impact on the reliability and safety of a rocket launch. Due to the limited measurement information inside the engine, it is often challenging to realize fast and accurate anomaly detection. For this reason, this paper introduces the rocket flight state data to expand the information source for anomaly detection. However, engine measurement and rocket flight state information have different data distribution characteristics. To find the optimal data fusion scheme for anomaly detection, a data set information fusion algorithm based on convex optimization is proposed, which solves the optimal fusion parameter using the convex quadratic programming problem and then adopts the adaptive CUSUM algorithm to realize the fast and accurate anomaly detection of engine faults. Numerical simulation tests show that the algorithm proposed in this paper has a higher detection accuracy and lower detection time than the traditional algorithm.<\/jats:p>","DOI":"10.3390\/s24020415","type":"journal-article","created":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T07:50:48Z","timestamp":1704873048000},"page":"415","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Anomaly Detection Method for Rocket Engines Based on Convex Optimized Information Fusion"],"prefix":"10.3390","volume":"24","author":[{"given":"Hao","family":"Sun","sequence":"first","affiliation":[{"name":"College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1985-0198","authenticated-orcid":false,"given":"Yuehua","family":"Cheng","sequence":"additional","affiliation":[{"name":"College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9153-4360","authenticated-orcid":false,"given":"Bin","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6620-2399","authenticated-orcid":false,"given":"Feng","family":"Lu","sequence":"additional","affiliation":[{"name":"College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Na","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"109837","DOI":"10.1016\/j.ress.2023.109837","article-title":"Dynamic Model-Assisted transferable network for Liquid Rocket Engine Fault Diagnosis using limited fault samples","volume":"243","author":"Wang","year":"2023","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Huang, P., Yu, H., and Wang, T. (2022). A Study Using Optimized LSSVR for Real-Time Fault Detection of Liquid Rocket Engine. Processe, 10.","DOI":"10.3390\/pr10081643"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"714","DOI":"10.1016\/j.actaastro.2020.08.019","article-title":"Deep neural network approach for fault detection and diagnosis during startup transient of liquid-propellant rocket engine","volume":"177","author":"Park","year":"2020","journal-title":"Acta Astronaut."},{"key":"ref_4","first-page":"1","article-title":"A supervised framework for recognition of liquid rocket engine health state under steady-state process without fault samples","volume":"70","author":"Lv","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_5","unstructured":"Oreilly, D. (1993). System for Anomaly and Failure Detection (SAFD) System Development (No. NAS 1.26: 193907), NASA."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Biggs, R. (1990, January 16\u201318). A probabilistic risk assessment for the space shuttle main engine with a turbomachinery vibration monitor cutoff system. Proceedings of the 26th Joint Propulsion Conference, Orlando, FL, USA.","DOI":"10.2514\/6.1990-2712"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wheeler, K., Dhawan, A., and Meyer, C. (1994, January 27\u201329). SSME sensor modeling using radial basis function neural networks. Proceedings of the 30th Joint Propulsion Conference and Exhibit, Indianapolis, IN, USA.","DOI":"10.2514\/6.1994-3229"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Yu, H., and Wang, T. (2021). A method for real-time fault detection of liquid rocket engine based on adaptive genetic algorithm optimizing back propagation neural network. Sensors, 21.","DOI":"10.3390\/s21155026"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1016\/j.actaastro.2020.11.035","article-title":"Data-driven fault detection in a reusable rocket engine using bivariate time-series analysis","volume":"179","author":"Tsutsumi","year":"2021","journal-title":"Acta Astronaut."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"112171","DOI":"10.1016\/j.measurement.2022.112171","article-title":"Retentive multimodal scale-variable anomaly detection framework with limited data groups for liquid rocket engine","volume":"205","author":"Zhang","year":"2022","journal-title":"Measurement"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.isatra.2022.07.014","article-title":"Memory-augmented skip-connected autoencoder for unsupervised anomaly detection of rocket engines with multi-source fusion","volume":"133","author":"Yan","year":"2023","journal-title":"ISA Trans."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"106861","DOI":"10.1016\/j.ymssp.2020.106861","article-title":"Multisensor data fusion for gearbox fault diagnosis using 2-D convolutional neural network and motor current signature analysis","volume":"144","author":"Azamfar","year":"2020","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Jiang, W., Xie, C., Zhuang, M., Shou, Y., and Tang, Y. (2016). Sensor data fusion with z-numbers and its application in fault diagnosis. Sensors, 16.","DOI":"10.3390\/s16091509"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1109\/TASE.2013.2250282","article-title":"A data-level fusion model for developing composite health indices for degradation modeling and prognostic analysis","volume":"10","author":"Liu","year":"2013","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"110506","DOI":"10.1016\/j.measurement.2021.110506","article-title":"Bearing fault diagnosis and prognosis using data fusion based feature extraction and feature selection","volume":"188","author":"Buchaiah","year":"2022","journal-title":"Measurement"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Jing, L., Wang, T., Zhao, M., and Wang, P. (2017). An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox. Sensors, 17.","DOI":"10.3390\/s17020414"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3533","DOI":"10.1109\/JSEN.2020.3026032","article-title":"Multi-feature fusion approach for epileptic seizure detection from EEG signals","volume":"21","author":"Radman","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_18","first-page":"8856818","article-title":"A multimodel decision fusion method based on DCNN-IDST for fault diagnosis of rolling bearing","volume":"2020","author":"Xu","year":"2020","journal-title":"Shock Vib."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1007\/s11431-021-1904-7","article-title":"Adaptive decision-level fusion strategy for the fault diagnosis of axial piston pumps using multiple channels of vibration signals","volume":"65","author":"Chao","year":"2022","journal-title":"Sci. China Technol. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"738","DOI":"10.1016\/j.jprocont.2012.02.003","article-title":"Decentralized fault detection and diagnosis via sparse PCA based decomposition and maximum entropy decision fusion","volume":"22","author":"Grbovic","year":"2012","journal-title":"J. Process Control"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1016\/j.isatra.2021.07.005","article-title":"Effective multi-sensor data fusion for chatter detection in milling process","volume":"125","author":"Tran","year":"2022","journal-title":"ISA Trans."},{"key":"ref_22","first-page":"102926","article-title":"Deep learning in multimodal remote sensing data fusion: A comprehensive review","volume":"112","author":"Li","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"9526","DOI":"10.1109\/TIM.2020.3003359","article-title":"Robust incipient fault detection of complex systems using data fusion","volume":"69","author":"Wei","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.paerosci.2019.03.002","article-title":"A survey of automatic control methods for liquid-propellant rocket engines","volume":"107","author":"Marzat","year":"2019","journal-title":"Prog. Aerosp. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1078","DOI":"10.2514\/1.G003518","article-title":"Optimal rocket landing guidance using convex optimization and model predictive control","volume":"42","author":"Wang","year":"2019","journal-title":"J. Guid. Control Dyn."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Sugimachi, T., Yonemoto, K., and Fujikawa, T. (2019, January 8). Attitude Control Law Design of Experimental Winged Rocket Using Engine Gimbal Control. Proceedings of the 2018 Asia-Pacific International Symposium on Aerospace Technology (APISAT 2018), Singapore.","DOI":"10.1007\/978-981-13-3305-7_194"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1007\/s42064-017-0003-8","article-title":"Survey of convex optimization for aerospace applications","volume":"1","author":"Liu","year":"2017","journal-title":"Astrodynamics"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1116","DOI":"10.2514\/1.G005376","article-title":"Convex approach to three-dimensional launch vehicle ascent trajectory optimization","volume":"44","author":"Benedikter","year":"2021","journal-title":"J. Guid. Control Dyn."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1208","DOI":"10.2514\/1.G000283","article-title":"Designing continuously constrained spacecraft relative trajectories for proximity operations","volume":"38","author":"Deaconu","year":"2015","journal-title":"J. Guid. Control Dyn."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"975","DOI":"10.1007\/s10957-013-0501-7","article-title":"Maximum divert for planetary landing using convex optimization","volume":"162","author":"Harris","year":"2014","journal-title":"J. Optim. Theory Appl."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"113558","DOI":"10.1016\/j.chaos.2023.113558","article-title":"Detrending moving-average cross-correlation based principal component analysis of air pollutant time series","volume":"172","author":"Dong","year":"2023","journal-title":"Chaos Solitons Fractals"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Berlo, B.V., Verhoeven, R., and Meratnia, N. (2023). Use of Domain Labels during Pre-Training for Domain-Independent WiFi-CSI Gesture Recognition. Sensors, 23.","DOI":"10.3390\/s23229233"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1137\/21M1402364","article-title":"Interior point methods can exploit structure of convex piecewise linear functions with application in radiation therapy","volume":"32","author":"Gorissen","year":"2022","journal-title":"SIAM J. Optim."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1540","DOI":"10.1080\/02664763.2020.1870670","article-title":"The optimized CUSUM and EWMA multi-charts for jointly detecting a range of mean and variance change","volume":"49","author":"Engmann","year":"2022","journal-title":"J. Appl. Stat."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"9469318","DOI":"10.1155\/2021\/9469318","article-title":"Vibration analysis for machine monitoring and diagnosis: A systematic review","volume":"2021","author":"Mohd","year":"2021","journal-title":"Shock Vib."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Rezatofighi, H., Nathan, T., Jun Young, G., Amir, S., Ian, R., and Silvio, S. (2019, January 15\u201320). Generalized intersection over union: A metric and a loss for bounding box regression. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00075"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/2\/415\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:43:35Z","timestamp":1760103815000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/2\/415"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,10]]},"references-count":36,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["s24020415"],"URL":"https:\/\/doi.org\/10.3390\/s24020415","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2024,1,10]]}}}