{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T14:12:14Z","timestamp":1765807934872,"version":"3.37.3"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"23","license":[{"start":{"date-parts":[[2021,7,17]],"date-time":"2021-07-17T00:00:00Z","timestamp":1626480000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,7,17]],"date-time":"2021-07-17T00:00:00Z","timestamp":1626480000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/R006768"],"award-info":[{"award-number":["EP\/R006768"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>This paper addresses the influence of manufacturing variability of a helicopter rotor blade on its aeroelastic responses. An aeroelastic analysis using finite elements in spatial and temporal domains is used to compute the helicopter rotor frequencies, vibratory hub loads, power required and stability in forward flight. The novelty of the work lies in the application of advanced data-driven machine learning (ML) techniques, such as convolution neural networks (CNN), multi-layer perceptron (MLP), random forests, support vector machines and adaptive Gaussian process (GP) for capturing the nonlinear responses of these complex spatio-temporal models to develop an efficient physics-informed ML framework for stochastic rotor analysis. Thus, the work is of practical significance as (i) it accounts for manufacturing uncertainties, (ii) accurately quantifies their effects on nonlinear response of rotor blade and (iii) makes the computationally expensive simulations viable by the use of ML. A rigorous performance assessment of the aforementioned approaches is presented by demonstrating validation on the training dataset and prediction on the test dataset. The contribution of the study lies in the following findings: (i) The uncertainty in composite material and geometric properties can lead to significant variations in the rotor aeroelastic responses and thereby highlighting that the consideration of manufacturing variability in analyzing helicopter rotors is crucial for assessing their behaviour in real-life scenarios. (ii) Precisely, the substantial effect of uncertainty has been observed on the six vibratory hub loads and the damping with the highest impact on the yawing hub moment. Therefore, sufficient factor of safety should be considered in the design to alleviate the effects of perturbation in the simulation results. (iii) Although advanced ML techniques are harder to train, the optimal model configuration is capable of approximating the nonlinear response trends accurately. GP and CNN followed by MLP achieved satisfactory performance. Excellent accuracy achieved by the above ML techniques demonstrates their potential for application in the optimization of rotors under uncertainty.<\/jats:p>","DOI":"10.1007\/s00521-021-06288-w","type":"journal-article","created":{"date-parts":[[2021,7,17]],"date-time":"2021-07-17T15:02:22Z","timestamp":1626534142000},"page":"16809-16828","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["The stochastic aeroelastic response analysis of helicopter rotors using deep and shallow machine learning"],"prefix":"10.1007","volume":"33","author":[{"given":"Tanmoy","family":"Chatterjee","sequence":"first","affiliation":[]},{"given":"Aniekan","family":"Essien","sequence":"additional","affiliation":[]},{"given":"Ranjan","family":"Ganguli","sequence":"additional","affiliation":[]},{"given":"Michael I.","family":"Friswell","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,7,17]]},"reference":[{"key":"6288_CR1","unstructured":"Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, others GS, Davis A, Dean J, Devin M, Ghemawat S, Goodfellow I, Harp A, Irving G, Isard M, Jia Y, Jozefowicz R, Kaiser L, Kudlur M, Levenberg J, Man\u00e9 D, Monga R, Moore S, Murray D, Olah C, Schuster M, Shlens J, Steiner B, Sutskever I, Talwar K, Tucker P, Vanhoucke V, Vasudevan V, Vi\u00e9gas F, Vinyals O, Warden P, Wattenberg M, Wicke M, Yu Y, Zheng X (2015) TensorFlow: large-scale machine learning on heterogeneous systems. http:\/\/tensorflow.org\/"},{"key":"6288_CR2","doi-asserted-by":"crossref","unstructured":"Adamson L, Fichera S, Mottershead J (2020) Aeroelastic stability analysis using stochastic structural modifications. J Sound Vib 477:115333","DOI":"10.1016\/j.jsv.2020.115333"},{"key":"6288_CR3","doi-asserted-by":"publisher","first-page":"704","DOI":"10.1016\/j.ast.2018.03.046","volume":"77","author":"A Batrakov","year":"2018","unstructured":"Batrakov A, Kusyumov A, Mikhailov S, Barakos G (2018) Aerodynamic optimization of helicopter rear fuselage. Aerosp Sci Technol 77:704\u2013712","journal-title":"Aerosp Sci Technol"},{"key":"6288_CR4","first-page":"153","volume":"19","author":"Y Bengio","year":"2007","unstructured":"Bengio Y, Lamblin P, Popovici D, Larochelle H et al (2007) Greedy layer-wise training of deep networks. Adv Neural Inform Process Syst 19:153","journal-title":"Adv Neural Inform Process Syst"},{"key":"6288_CR5","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1146\/annurev-fluid-122414-034441","volume":"49","author":"P Beran","year":"2017","unstructured":"Beran P, Stanford B, Schrock C (2017) Uncertainty quantification in aeroelasticity. Ann Rev Fluid Mech 49:361\u2013386","journal-title":"Ann Rev Fluid Mech"},{"key":"6288_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2016\/1302564","volume":"2016","author":"G Bernerdini","year":"2016","unstructured":"Bernerdini G, Piccione E, Anobile A, Serafini J, Gennaretti M (2016) Optimal design and acoustic assessment of low-vibration rotor blades. Int J Rotat Machin 2016:1\u201317","journal-title":"Int J Rotat Machin"},{"key":"6288_CR7","unstructured":"Bir G, Chopra I, Ganguli R (1992) University of Maryland advanced rotorcraft code UMARC theory manual. Tech rep, UM-AERO Report 92-02, Center for Rotorcraft Education and Research, University of Maryland, College Park"},{"issue":"1","key":"6288_CR8","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1016\/0898-1221(86)90092-1","volume":"12","author":"M Borri","year":"1986","unstructured":"Borri M (1986) Helicopter rotor dynamics by finite element time approximation. Comput Math Appl 12(1):149\u2013160","journal-title":"Comput Math Appl"},{"key":"6288_CR9","doi-asserted-by":"publisher","unstructured":"Boser B, Guyon I, Vapnik V (1992) A training algorithm for optimal margin classifiers. In: Proceedings of the fifth annual workshop on Computational learning theory, pp 144\u2013152, isbn: 089791497X. Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/130385.130401","DOI":"10.1145\/130385.130401"},{"key":"6288_CR10","doi-asserted-by":"crossref","unstructured":"Brink A, Najera-Flores D, Martinez C (2021) The neural network collocation method for solving partial differential equations. Neural Comput Appl 33:5591\u20135608","DOI":"10.1007\/s00521-020-05340-5"},{"issue":"1","key":"6288_CR11","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1023\/A:1007379606734","volume":"28","author":"R Caruana","year":"1997","unstructured":"Caruana R (1997) Multitask learning. Mach Learn 28(1):41\u201375","journal-title":"Mach Learn"},{"key":"6288_CR12","first-page":"1","volume":"14","author":"J Chassaing","year":"2018","unstructured":"Chassaing J, Nitschke C, Vincenti A, Cinnella P, Lucor D (2018) Advances in parametric and model-form uncertainty quantification in canonical aeroelastic systems. J Aerosp Lab 14:1\u201319","journal-title":"J Aerosp Lab"},{"key":"6288_CR13","doi-asserted-by":"publisher","first-page":"572","DOI":"10.1016\/j.cma.2018.01.011","volume":"332","author":"T Chatterjee","year":"2018","unstructured":"Chatterjee T, Chowdhury R (2018) h - p adaptive model based approximation of moment free sensitivity indices. Comput Methods Appl Mech Eng 332:572\u2013599","journal-title":"Comput Methods Appl Mech Eng"},{"key":"6288_CR14","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1016\/j.ymssp.2015.09.001","volume":"70\u201371","author":"T Chatterjee","year":"2016","unstructured":"Chatterjee T, Chakraborty S, Chowdhury R (2016) A bi-level approximation tool for the computation of FRFs in stochastic dynamic systems. Mech Syst Signal Process 70\u201371:484\u2013505","journal-title":"Mech Syst Signal Process"},{"issue":"1","key":"6288_CR15","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1007\/s11831-017-9240-5","volume":"26","author":"T Chatterjee","year":"2019","unstructured":"Chatterjee T, Chakraborty S, Chowdhury R (2019) A critical review of surrogate assisted robust design optimization. Archiv Comput Methods Eng 26(1):245\u2013274","journal-title":"Archiv Comput Methods Eng"},{"issue":"3","key":"6288_CR16","first-page":"273","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273\u2013297","journal-title":"Mach Learn"},{"key":"6288_CR17","doi-asserted-by":"publisher","unstructured":"Daniel M, Brewer W, Behm G, Strelzoff A, Wilson A, Wade D (2018) Deep learning evolutionary optimization for regression of rotorcraft vibrational spectra. In: IEEE\/ACM Machine Learning in HPC Environments (MLHPC), Dallas, TX, USA. https:\/\/doi.org\/10.1109\/MLHPC.2018.8638645","DOI":"10.1109\/MLHPC.2018.8638645"},{"key":"6288_CR18","unstructured":"Dempsey P, Branning J, Wade D, Bolander N (2010) Comparison of test stand and helicopter oil cooler bearing condition indicators. In: Proceedings of the American Helicopter Society 66th Annual Forum and Technology, Phoenix, AZ"},{"key":"6288_CR19","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1016\/j.compstruct.2017.01.061","volume":"171","author":"S Dey","year":"2017","unstructured":"Dey S, Mukhopadhyay T, Adhikari S (2017) Metamodel based high-fidelity stochastic analysis of composite laminates: a concise review with critical comparative assessment. Compos Struct 171:227\u2013250","journal-title":"Compos Struct"},{"key":"6288_CR20","first-page":"155","volume":"9","author":"H Drucker","year":"1997","unstructured":"Drucker H, Burges C, Kaufman L, Smola A, Vapnik V et al (1997) Support vector regression machines. Adv Neural Inform Process Syst 9:155\u2013161","journal-title":"Adv Neural Inform Process Syst"},{"issue":"2","key":"6288_CR21","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1006\/jsvi.2002.5179","volume":"258","author":"R Ganguli","year":"2002","unstructured":"Ganguli R (2002) Optimum design of a helicopter rotor for low vibration using aeroelastic analysis and response surface methods. J Sound Vib 258(2):327\u2013344","journal-title":"J Sound Vib"},{"key":"6288_CR22","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1016\/j.ast.2018.07.013","volume":"80","author":"M Gennaretti","year":"2018","unstructured":"Gennaretti M, Bernardini G, Serafini J, Romani G (2018) Rotorcraft comprehensive code assessment for blade-vortex interaction conditions. Aerosp Sci Technol 80:232\u2013246","journal-title":"Aerosp Sci Technol"},{"key":"6288_CR23","unstructured":"Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT press, Cambridge, MA, USA. http:\/\/www.deeplearningbook.org"},{"key":"6288_CR24","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-020-05035-x","author":"K Hamdia","year":"2020","unstructured":"Hamdia K, Zhuang X, Rabczuk T (2020) An efficient optimization approach for designing machine learning models based on genetic algorithm. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-020-05035-x","journal-title":"Neural Comput Appl"},{"issue":"2","key":"6288_CR25","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1016\/0893-6080(88)90015-9","volume":"1","author":"R Hecht-Nielsen","year":"1988","unstructured":"Hecht-Nielsen R (1988) Applications of counterpropagation networks. Neural Networks 1(2):131\u2013139","journal-title":"Neural Networks"},{"issue":"7","key":"6288_CR26","doi-asserted-by":"publisher","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","volume":"18","author":"G Hinton","year":"2006","unstructured":"Hinton G, Osindero S, Teh Y (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527\u20131554","journal-title":"Neural Comput"},{"issue":"105","key":"6288_CR27","first-page":"592","volume":"97","author":"S Kontogiannis","year":"2020","unstructured":"Kontogiannis S, Demange J, Savill A, Kipouros T (2020) A comparison study of two multifidelity methods for aerodynamic optimization. Aerosp Sci Technol 97(105):592","journal-title":"Aerosp Sci Technol"},{"issue":"6","key":"6288_CR28","first-page":"119","volume":"52","author":"DG Krige","year":"1951","unstructured":"Krige DG (1951) A statistical approach to some basic mine valuation problems on the witwatersrand. J Chem Metall Min Soc South Africa 52(6):119\u2013139","journal-title":"J Chem Metall Min Soc South Africa"},{"issue":"2","key":"6288_CR29","doi-asserted-by":"crossref","first-page":"584","DOI":"10.1016\/j.amc.2010.01.081","volume":"216","author":"R Kumar","year":"2010","unstructured":"Kumar R, Ganguli R, Omkar SN (2010) Rotorcraft parameter estimation using radial basis function neural network. Appl Math Comput 216(2):584\u2013597","journal-title":"Appl Math Comput"},{"issue":"2","key":"6288_CR30","doi-asserted-by":"publisher","first-page":"164","DOI":"10.4050\/JAHS.51.164","volume":"51","author":"V Kumar","year":"2006","unstructured":"Kumar V, Omkar S, Ganguli R, Sampath P, Suresh S (2006) Identification of helicopter dynamics using recurrent neural networks and flight data. J Am Helicopter Soc 51(2):164\u2013174","journal-title":"J Am Helicopter Soc"},{"key":"6288_CR31","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-020-05258-y","author":"F Li","year":"2020","unstructured":"Li F, Gao L, Garg A, Shen W, Huang S (2020) A comparative study of pre-screening strategies within a surrogate-assisted multi-objective algorithm framework for computationally expensive problems. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-020-05258-y","journal-title":"Neural Comput Appl"},{"key":"6288_CR32","unstructured":"Li L (2007) Structural Design of Composite Rotor Blades with Consideration of Manufacturability, Durability, and Manufacturing Uncertainties. PhD thesis, Georgia Institute of Technology. http:\/\/hdl.handle.net\/1853\/24757"},{"key":"6288_CR33","unstructured":"Lophaven S, Nielson H, Sondergaard J (2002) DACE A MATLAB Kriging Toolbox.\u00a0Computer programme, Informatics and Mathematical Modelling, Technical University of Denmark, IMM-TR-2002-12. http:\/\/www2.imm.dtu.dk\/pubdb\/p.php?1460"},{"key":"6288_CR34","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1016\/j.ast.2019.03.005","volume":"88","author":"Y Lu","year":"2019","unstructured":"Lu Y, Su T, Chen R, Li P, Wang Y (2019) A method for optimizing the aerodynamic layout of a helicopter that reduces the effects of aerodynamic interaction. Aerosp Sci Technol 88:73\u201383","journal-title":"Aerosp Sci Technol"},{"issue":"9","key":"6288_CR35","doi-asserted-by":"publisher","first-page":"1243","DOI":"10.1080\/0305215X.2014.958734","volume":"47","author":"R Mallick","year":"2015","unstructured":"Mallick R, Ganguli R, Bhat M (2015) Robust design of multiple trailing edge flaps for helicopter vibration reduction: a multi-objective bat algorithm approach. Eng Optim 47(9):1243\u20131263","journal-title":"Eng Optim"},{"key":"6288_CR36","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1016\/j.jpdc.2019.08.008","volume":"135","author":"D Mart\u00ednez","year":"2020","unstructured":"Mart\u00ednez D, Brewer W, Strelzoff A, Wilson A, Wade D (2020) Rotorcraft virtual sensors via deep regression. J Parallel Distrib Comput 135:114\u2013126","journal-title":"J Parallel Distrib Comput"},{"issue":"2","key":"6288_CR37","first-page":"239","volume":"21","author":"M McKay","year":"1979","unstructured":"McKay M, Beckman RJ, Conover WJ (1979) A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21(2):239\u2013245","journal-title":"Technometrics"},{"issue":"2","key":"6288_CR38","first-page":"04018","volume":"4","author":"M Moustapha","year":"2018","unstructured":"Moustapha M, Bourinet JM, Guillaume B, Sudret B (2018) Comparative study of kriging and support vector regression for structural engineering applications. J Uncertain Eng Syst Part A Civ Eng 4(2):04018","journal-title":"J Uncertain Eng Syst Part A Civ Eng"},{"issue":"9","key":"6288_CR39","doi-asserted-by":"publisher","first-page":"2332","DOI":"10.2514\/1.35941","volume":"46","author":"S Murugan","year":"2008","unstructured":"Murugan S, Harursampath Ganguli R (2008) Material uncertainty propagation in helicopter nonlinear aeroelastic response and vibratory analysis. AIAA J 46(9):2332\u20132344","journal-title":"AIAA J"},{"issue":"1","key":"6288_CR40","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.ast.2011.02.004","volume":"16","author":"S Murugan","year":"2012","unstructured":"Murugan S, Chowdhury R, Adhikari S, Friswell M (2012) Helicopter aeroelastic analysis with spatially uncertain rotor blade properties. Aerosp Sci Technol 16(1):29\u201339","journal-title":"Aerosp Sci Technol"},{"key":"6288_CR41","doi-asserted-by":"publisher","first-page":"1269","DOI":"10.1016\/S0020-7462(02)00072-0","volume":"38","author":"G Muscolino","year":"2003","unstructured":"Muscolino G, Ricciardi G, Cacciola P (2003) Monte carlo simulation in the stochastic analysis of non-linear systems under external stationary poisson white noise input. Int J Nonlinear Mech 38:1269\u20131283","journal-title":"Int J Nonlinear Mech"},{"issue":"3","key":"6288_CR42","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1016\/j.compstruct.2004.08.037","volume":"70","author":"A Onkar","year":"2005","unstructured":"Onkar A, Yadav D (2005) Forced nonlinear vibration of laminated composite plates with random material properties. Compos Struct 70(3):334\u2013342","journal-title":"Compos Struct"},{"issue":"5","key":"6288_CR43","doi-asserted-by":"publisher","first-page":"1217","DOI":"10.2514\/1.3961","volume":"41","author":"C Pettit","year":"2004","unstructured":"Pettit C (2004) Uncertainty quantification in aeroelasticity: recent results and research challenges. J Aircraft 41(5):1217\u20131229","journal-title":"J Aircraft"},{"key":"6288_CR44","unstructured":"Pflumm T, Rex W, Hajek M (2019) Propagation of Material and Manufacturing Uncertainties in Composite Helicopter Rotor Blades. In: 45th European Rotorcraft Forum, Warsaw, Poland"},{"key":"6288_CR45","volume-title":"Gaussian processes for machine learning","author":"CE Rasmussen","year":"2006","unstructured":"Rasmussen CE, Williams CKI (2006) Gaussian processes for machine learning. The MIT Press, Cambridge, Massachusetts London, England"},{"key":"6288_CR46","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-020-05419-z","author":"I Roman","year":"2020","unstructured":"Roman I, Santana R, Mendiburu A, Lozano J (2020) In-depth analysis of svm kernel learning and its components. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-020-05419-z","journal-title":"Neural Comput Appl"},{"key":"6288_CR47","doi-asserted-by":"publisher","unstructured":"Rumelhart D, Hinton G, Williams R (1986) Learning representations by back-propagating errors. Nature 323:533\u2013536. https:\/\/doi.org\/10.1038\/323533a0","DOI":"10.1038\/323533a0"},{"issue":"2","key":"6288_CR48","doi-asserted-by":"publisher","first-page":"553","DOI":"10.2514\/1.C031156","volume":"48","author":"K Saijal","year":"2011","unstructured":"Saijal K, Ganguli R, Viswamurthy SR (2011) Optimization of helicopter rotor using polynomial and neural network metamodels. J Aircraft 48(2):553\u2013566","journal-title":"J Aircraft"},{"key":"6288_CR49","doi-asserted-by":"publisher","unstructured":"Vladimir N. Vapnik (1995) The nature of statistical learning theory. Springer, New York, NY. https:\/\/doi.org\/10.1007\/978-1-4757-2440-0","DOI":"10.1007\/978-1-4757-2440-0"},{"key":"6288_CR50","unstructured":"Sener O, Koltun V (2018) Multi-task learning as multi-objective optimization. In:\u00a0NIPS'18: Proceedings of the 32nd International Conference on Neural Information Processing Systems, pp 525\u2013536. Curran Associates Inc., Red Hook, NY, United States"},{"key":"6288_CR51","doi-asserted-by":"crossref","unstructured":"Sudret B (2012) Meta-models for structural reliability and uncertainty quantification. In: Proceedings of 5th Asian-Pacific Symposium on Stuctural Reliabilty and its Applications (APSSRA, 2012), Singapore, pp 53\u201376, ID: hal-00683179","DOI":"10.3850\/978-981-07-2219-7_P321"},{"issue":"6","key":"6288_CR52","first-page":"774","volume":"24","author":"V Vapnik","year":"1963","unstructured":"Vapnik V, Lerner A (1963) Generalized portrait method for pattern recognition. Autom Remote Control 24(6):774\u2013780","journal-title":"Autom Remote Control"},{"key":"6288_CR53","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.advengsoft.2016.06.005","volume":"100","author":"N Vu-Bac","year":"2016","unstructured":"Vu-Bac N, Lahmer T, Zhuang X, Nguyen-Thoi T, Rabczuk T (2016) A software framework for probabilistic sensitivity analysis for computationally expensive models. Adv Eng Softw 100:19\u201331","journal-title":"Adv Eng Softw"},{"key":"6288_CR54","unstructured":"Wade D, Wilson A (2017) Applying machine learning-based diagnostic functions to rotorcraft safety. In: 17th Australian International Aerospace Congress: AIAC 2017, Engineers Australia, Royal Aeronautical Society, pp 663\u2013669"},{"key":"6288_CR55","unstructured":"Wade D, Vongpaseuth T, Lugos R, Ayscue J, Wilson A, Antolick L, et al (2015) Machine learning algorithms for hums improvement on rotorcraft components. In: Proceedings of the 71st Annual Forum of the American Helicopter Society, Virginia Beach, Virginia"},{"key":"6288_CR56","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.compind.2018.12.001","volume":"108","author":"C Wu","year":"2019","unstructured":"Wu C, Jiang P, Ding C, Feng F, Chen T (2019) Intelligent fault diagnosis of rotating machinery based on one-dimensional convolutional neural network. Comput Ind 108:53\u201361","journal-title":"Comput Ind"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06288-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-06288-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06288-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,6]],"date-time":"2023-11-06T05:19:22Z","timestamp":1699247962000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-06288-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,17]]},"references-count":56,"journal-issue":{"issue":"23","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["6288"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-06288-w","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2021,7,17]]},"assertion":[{"value":"5 December 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 June 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 July 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no conflicts of interest to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}