nach oben

Neural Computing and Applications

Erschienen in:

04.03.2017 | Neural Computing in Next Generation Virtual Reality Technology

Numerical research on virtual reality of vibration characteristics of the motor based on GA-BPNN model

verfasst von: Xin-ya Chen, Zhen Chen, Yang Zhao

Erschienen in: Neural Computing and Applications | Ausgabe 5/2018

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

This paper firstly established the finite element model of steel shell motor, computed modal frequencies on top 6 orders to compare with experimental results and verified the reliability of the finite element model. Then, this paper numerically calculated the electromagnetic force of the motor, inputted it into the verified finite element model and computed the vibration acceleration, velocity, stress and strain of the motor. Constraint and properties of internal materials remained unchanged. Steel shell was replaced by aluminum alloy shell to recompute the vibration acceleration, velocity, stress and strain of the motor and compare with those of steel structure motor. Results showed that the motor of aluminum alloy shell had more obvious vibration characteristics. Finally, this paper put forward neural network model optimized by GA. This model was used to predict the vibration characteristics of the motor of aluminum alloy shell and compare with the real value calculated by finite element, showing good consistency. It indicated that it was feasible to predict the vibration characteristics of the motor based on GA-BPNN model.

Vorheriger Artikel Reality of virtual damage identification based on neural networks and vibration analysis of a damaged bridge under a moving vehicle

Nächster Artikel Optimization design and reality of the virtual cutting process for the boring bar based on PSO-BP neural networks

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Sun T, Kim JM, Lee GH et al (2011) Effect of pole and slot combination on noise and vibration in permanent magnet synchronous motor. IEEE Trans Magn 47(5):1038–1041CrossRef

Shin PS, Cheung HJ (2011) A magnetostrictive force and vibration mode analysis of 3 kW BLDC motor by a magneto-mechanical coupling formulation. J Electr Eng Technol 6(1):76–80CrossRef

Pollock RD, Woledge RC, Martin FC et al (2012) Effects of whole body vibration on motor unit recruitment and threshold. J Appl Physiol 112(3):388–395CrossRef

McBride JM, Nuzzo JL, Dayne AM et al (2010) Effect of an acute bout of whole body vibration exercise on muscle force output and motor neuron excitability. J Strength Cond Res 24(1):184–189CrossRef

Qiu JJ (2002) Investigation on coupled mechanical and electrical vibration and coupled magnetical and solid vibration of electrical machine. Proc Chin Soc Electr Eng 5(5):109–115

Qiu JJ (1981) Study of stability of motion shock absorption and parameters vibration of rotor of steam turbine generator excited by electromagnetic forces. J Tianjin Univ 4:83–96

Wu HM, Jia QF (2011) Research on nonlinear vibration of the motor rigid body model. J Dyn Control 9(3):222–226

Ma CG, Zuo SG, Tan QW et al (2013) Nonlinear torsional vibration model of a PMSM for electric driven vehicle. J Vib Shock 32(12):131–134

Tomczuk B, Sobo M (2005) A field-network model of a linear oscillating motor and its dynamics characteristics. Magnetics 8:2362–2367CrossRef

10.

Lv Z, Chen G, Zhong C et al (2012) A framework for multi-dimensional webgis based interactive online virtual community. Adv Sci Lett 7(1):215–219CrossRef

11.

Lv Z, Halawani A, Feng S et al (2015) Touch-less interactive augmented reality game on vision-based wearable device. Pers Ubiquitous Comput 19(3–4):551–567CrossRef

12.

Guo Z, Wu J, Lu H et al (2011) A case study on a hybrid wind speed forecasting method using BP neural network. Knowl Based Syst 24(7):1048–1056CrossRef

13.

Ding S, Su C, Yu J (2011) An optimizing BP neural network algorithm based on genetic algorithm. Artif Intell Rev 36(2):153–162CrossRef

14.

Kourehli SS (2015) Damage quantification method using artificial neural network and static response with limited sensors. J Vibroeng 17(3):1317–1325

15.

Qian K, Liang J, Gao Y (2015) The prediction of vibration and noise for the high-speed train based on neural network and boundary element method. J Vibroeng 17(8):4445–4457

16.

Shuran L, Shujin L (2011) Applying BP neural network model to forecast peak velocity of blasting ground vibration. Proced Eng 26:257–263CrossRef

17.

Wei Wei, Yong Qi (2011) Information potential fields navigation in wireless Ad-Hoc sensor networks. Sensors 11(5):4794–4807MathSciNetCrossRef

18.

Chen Z, Li C, Sánchez RV (2015) Multi-layer neural network with deep belief network for gearbox fault diagnosis. J Vibroeng 17(5):2379–2392

19.

Hui KH, Ooi CS, Lim MH et al (2016) A hybrid artificial neural network with Dempster-Shafer theory for automated bearing fault diagnosis. J Vibroeng 18(7):4409–4418CrossRef

20.

Ke L, Wenyan G, Xiaoliu S et al (2012) Research on the forecast model of electricity power industry loan based on GA-BP neural network. Energy Proced 14:1918–1924CrossRef

21.

Wang Y, Wang S, Zhang N (2013) A novel wind speed forecasting method based on ensemble empirical mode decomposition and GA-BP neural network. In: Power and energy society general meeting (PES), 2013 IEEE. IEEE, pp 1–5

22.

Xie S, Zhou H, Zhao J et al (2013) Energy-absorption forecast of thin-walled structure by GA-BP hybrid algorithm. J Cent South Univ 20(4):1122–1128CrossRef

Titel: Numerical research on virtual reality of vibration characteristics of the motor based on GA-BPNN model
verfasst von: Xin-ya Chen
Zhen Chen
Yang Zhao
Publikationsdatum: 04.03.2017
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 5/2018
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-017-2923-x

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Weitere Artikel der Ausgabe 5/2018

Development of adaptive p-step RBF network model with recursive orthogonal least squares training

Multipoint infrared laser-based detection and tracking for people counting

Virtual reality of interior noises of vehicles based on boundary element and neural networks

Simplified neural network for generalized least absolute deviation

Virtual reality of recognition technologies of the improved contour coding image based on level set and neural network models

Fast and robust absolute camera pose estimation with known focal length