{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T09:30:57Z","timestamp":1770283857808,"version":"3.49.0"},"reference-count":24,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2023,7,7]],"date-time":"2023-07-07T00:00:00Z","timestamp":1688688000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Natural Science Foundation of China Key Support Project","award":["U2133202"],"award-info":[{"award-number":["U2133202"]}]},{"name":"the National Natural Science Foundation of China Key Support Project","award":["2022M720955"],"award-info":[{"award-number":["2022M720955"]}]},{"name":"the National Natural Science Foundation of China Key Support Project","award":["LBH-Z22187"],"award-info":[{"award-number":["LBH-Z22187"]}]},{"name":"the Fellowship of China Postdoctoral Science Foundation","award":["U2133202"],"award-info":[{"award-number":["U2133202"]}]},{"name":"the Fellowship of China Postdoctoral Science Foundation","award":["2022M720955"],"award-info":[{"award-number":["2022M720955"]}]},{"name":"the Fellowship of China Postdoctoral Science Foundation","award":["LBH-Z22187"],"award-info":[{"award-number":["LBH-Z22187"]}]},{"name":"the Fellowship of Heilongjiang Province Postdoctoral Science Foundation","award":["U2133202"],"award-info":[{"award-number":["U2133202"]}]},{"name":"the Fellowship of Heilongjiang Province Postdoctoral Science Foundation","award":["2022M720955"],"award-info":[{"award-number":["2022M720955"]}]},{"name":"the Fellowship of Heilongjiang Province Postdoctoral Science Foundation","award":["LBH-Z22187"],"award-info":[{"award-number":["LBH-Z22187"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The landing gear structure suffers from large loads during aircraft takeoff and landing, and an accurate prediction of landing gear performance is beneficial to ensure flight safety. Nevertheless, the landing gear performance prediction method based on machine learning has a strong reliance on the dataset, in which the feature dimension and data distribution will have a great impact on the prediction accuracy. To address these issues, a novel MCA-MLPSA is developed. First, an MCA (multiple correlation analysis) method is proposed to select key features. Second, a heterogeneous multilearner integration framework is proposed, which makes use of different base learners. Third, an MLPSA (multilayer perceptron with self-attention) model is proposed to adaptively capture the data distribution and adjust the weights of each base learner. Finally, the excellent prediction performance of the proposed MCA-MLPSA is validated by a series of experiments on the landing gear data.<\/jats:p>","DOI":"10.3390\/s23136219","type":"journal-article","created":{"date-parts":[[2023,7,10]],"date-time":"2023-07-10T00:54:23Z","timestamp":1688950463000},"page":"6219","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A Self-Attention Integrated Learning Model for Landing Gear Performance Prediction"],"prefix":"10.3390","volume":"23","author":[{"given":"Lin","family":"Lin","sequence":"first","affiliation":[{"name":"School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China"}]},{"given":"Changsheng","family":"Tong","sequence":"additional","affiliation":[{"name":"School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China"}]},{"given":"Feng","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China"}]},{"given":"Song","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China"}]},{"given":"Yancheng","family":"Lv","sequence":"additional","affiliation":[{"name":"School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China"}]},{"given":"Wenhui","family":"He","sequence":"additional","affiliation":[{"name":"School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Chen, J., Xu, Q., Guo, Y., and Chen, R. (2022). Aircraft Landing Gear Retraction\/Extension System Fault Diagnosis with 1-D Dilated Convolutional Neural Network. Sensors, 22.","DOI":"10.3390\/s22041367"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Cutolo, A., Bernini, R., Berruti, G.M., Breglio, G., Bruno, F.A., Buontempo, S., Catalano, E., Consales, M., Coscetta, A., and Cusano, A. (2023). Innovative Photonic Sensors for Safety and Security, Part II: Aerospace and Submarine Applications. Sensors, 23.","DOI":"10.3390\/s23052417"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.engfailanal.2019.03.010","article-title":"Failure analysis of the nose landing gear axle of an aircraft","volume":"101","author":"Freitas","year":"2019","journal-title":"Eng. Fail. Anal."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"105015","DOI":"10.1016\/j.engfailanal.2020.105015","article-title":"Mechanism analysis of a main landing gear of transporting aircraft: A design learning perspective","volume":"119","author":"Kadarno","year":"2021","journal-title":"Eng. Fail. Anal."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.cja.2021.08.038","article-title":"Bifurcation analysis of dual-sidestay landing gear locking performance considering joint clearance","volume":"35","author":"Xu","year":"2022","journal-title":"Chin. J. Aeronaut."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.ast.2019.05.026","article-title":"Investigation of gear walk suppression while maintaining braking performance in a main landing gear","volume":"91","author":"Yin","year":"2019","journal-title":"Aerosp. Sci. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1609","DOI":"10.14716\/ijtech.v10i8.3486","article-title":"Analysis of the static behavior of a new landing gear model based on a four-bar linkage mechanism","volume":"10","author":"Son","year":"2019","journal-title":"Int. J. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1785","DOI":"10.21595\/jve.2021.21915","article-title":"Investigation of random runway effect on landing of an aircraft with active landing gears using nonlinear mathematical model","volume":"23","author":"Sivakumar","year":"2021","journal-title":"J. Vibroeng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3700","DOI":"10.1177\/0954410018804093","article-title":"Design and dynamic analysis of landing gear system in vertical takeoff and vertical landing reusable launch vehicle","volume":"233","author":"Zhang","year":"2019","journal-title":"Proc. Inst. Mech. Eng. Part G J. Aerosp. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3446","DOI":"10.1109\/LRA.2023.3266987","article-title":"Learning-based Distortion Compensation for a Hybrid Simulator of Space Docking","volume":"8","author":"Qi","year":"2023","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4351","DOI":"10.1109\/TIE.2020.2984968","article-title":"Deep learning-based partial domain adaptation method on intelligent machinery fault diagnostics","volume":"68","author":"Li","year":"2020","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"108223","DOI":"10.1016\/j.ress.2021.108223","article-title":"Machine learning-based methods in structural reliability analysis: A review","volume":"219","author":"Afshari","year":"2022","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"110407","DOI":"10.1016\/j.measurement.2021.110407","article-title":"MLPC-CNN: A multi-sensor vibration signal fault diagnosis method under less computing resources","volume":"188","author":"Zhang","year":"2022","journal-title":"Measurement"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"108205","DOI":"10.1016\/j.measurement.2020.108205","article-title":"Long-term gear life prediction based on ordered neurons LSTM neural networks","volume":"165","author":"Yan","year":"2020","journal-title":"Measurement"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"111582","DOI":"10.1016\/j.measurement.2022.111582","article-title":"Experimental investigation on cavitation and cavitation detection of axial piston pump based on MLP-Mixer","volume":"200","author":"Lan","year":"2022","journal-title":"Measurement"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"111737","DOI":"10.1016\/j.measurement.2022.111737","article-title":"Partial discharge ultrasonic signals pattern recognition in transformer using BSO-SVM based on microfiber coupler sensor","volume":"201","author":"Zhou","year":"2022","journal-title":"Measurement"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3462","DOI":"10.1109\/TEC.2021.3075897","article-title":"Wind turbine gearbox anomaly detection based on adaptive threshold and twin support vector machines","volume":"36","author":"Dhiman","year":"2021","journal-title":"IEEE Trans. Energy Convers."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Qiang, S., Lin, H., and Yu, Z. (2016, January 19\u201321). Online faults diagnosis of wind turbine blades based on support vector machines. Proceedings of the 2016 3rd International Conference on Systems and Informatics (ICSAI), Shanghai, China.","DOI":"10.1109\/ICSAI.2016.7810962"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"108012","DOI":"10.1016\/j.ress.2021.108012","article-title":"Deep residual LSTM with domain-invariance for remaining useful life prediction across domains","volume":"216","author":"Fu","year":"2021","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"7230","DOI":"10.1109\/TII.2021.3121326","article-title":"Spatiotemporally multidifferential processing deep neural network and its application to equipment remaining useful life prediction","volume":"18","author":"Xiang","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"101535","DOI":"10.1016\/j.aei.2022.101535","article-title":"Highly imbalanced fault diagnosis of mechanical systems based on wavelet packet distortion and convolutional neural networks","volume":"51","author":"Zhao","year":"2022","journal-title":"Adv. Eng. Inform."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Chandrahas, N.S., Choudhary, B.S., Teja, M.V., Venkataramayya, M.S., and Prasad, N.S.R.K. (2022). XG Boost Algorithm to Simultaneous Prediction of Rock Fragmentation and Induced Ground Vibration Using Unique Blast Data. Appl. Sci., 12.","DOI":"10.3390\/app12105269"},{"key":"ref_23","first-page":"2","article-title":"State of health and charge estimation based on adaptive boosting integrated with particle swarm optimization\/support vector machine (AdaBoost-PSO-SVM) Model for Lithium-ion Batteries","volume":"17","author":"Li","year":"2022","journal-title":"Int. J. Electrochem. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.enbuild.2019.04.018","article-title":"Modal decomposition based ensemble learning for ground source heat pump systems load forecasting","volume":"194","author":"Xu","year":"2019","journal-title":"Energy Build."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/13\/6219\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:08:01Z","timestamp":1760126881000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/13\/6219"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,7]]},"references-count":24,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["s23136219"],"URL":"https:\/\/doi.org\/10.3390\/s23136219","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,7]]}}}