Top

Published in:

2019 | OriginalPaper | Chapter

Unbalance Vibration Compensation Control Using Deep Network for Rotor System with Active Magnetic Bearings

Authors : Xuan Yao, Zhaobo Chen, Yinghou Jiao

Published in: Proceedings of the 10th International Conference on Rotor Dynamics – IFToMM

Publisher: Springer International Publishing

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Unbalance vibration directly affects the operational precision, stability and life of rotary machinery. Profiting from the active control speciality of active magnetic bearing (AMB), unbalance vibration of rotor system with AMBs can be compensated and controlled automatically. This paper considers unbalance vibration minimum for rotor system with AMBs. Deep learning theory is utilized to design a compensation controller, which is added to the PID feedback control. The structure of the compensation controller is established by a deep neural network with 2 hidden layers, and its operation algorithms are designed. Model of a 4-DOF rigid rotor with AMBs is established for controller parameter setting and simulation. The unbalance vibration control of different controllers at fixed rotational speed is simulated, and the control effects of the proposed controller are demonstrated via unbalance vibration analysis and control current analysis. This research provides a new adaptive control approach for AMB control of unbalance minimum compensation, and it can also be applied in other multi-dimension vibration control.

Dont have a licence yet? Then find out more about our products and how to get one now:

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!

inform now

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!

inform now

previous chapter Dynamical Characteristic Analysis of Elastic Ring Squeeze Film Damper in Rotor System

next chapter Unbalance Identification in a Rotor Supported by Active Magnetic Bearing

Schweitzer, G., Maslen, E.H.: Magnetic Bearings: Theory, Design, and Application to Rotating Machinery. Springer, Berlin (2009)

Chen, Q., Liu, G., Han, B.: Unbalance vibration suppression for AMBs system using adaptive notch filter. Mech. Syst. Signal Process. 93, 136–150 (2017)CrossRef

Hui, C., Shi, L., Wang, J., Yu, S.: Adaptive unbalance vibration control of active magnetic bearing systems for the HTR-10GT. In: International Conference on Nuclear Engineering, pp. 793–801. ASME, Xi’an (2010)

He, Y., Shi, L., Shi, Z., Sun, Z.: Unbalance compensation of a full scale test rig designed for HTR-10GT: a frequency-domain approach based on iterative learning control. Science and Technology and Nuclear Installations, pp. 1–15 (2017)

Tung, P.C., Tsai, M.T., Chen, K.Y., Fan, Y.H., Chou, F.C.: Design of model-based unbalance compensator with fuzzy gain tuning mechanism for an active magnetic bearing system. Expert Syst. Appl. 38(10), 12861–12868 (2011)CrossRef

Kuseyri, İ.S.: Robust control and unbalance compensation of rotor/active magnetic bearing systems. J. Vib. Control 18(6), 817–832 (2012)MathSciNetCrossRef

Fang, J., Xu, X., Xie, J.: Active vibration control of rotor imbalance in active magnetic bearing systems. J. Vib. Control 21(4), 684–700 (2013)CrossRef

Okubo, S., Nakamura, Y., Wakui, S.: Unbalance vibration control for active magnetic bearing using automatic balancing system and peak-of-gain control. In: IEEE International Conference on Mechatronics, vol. 307, pp. 105–110. IEEE, Vicenza (2013)

Heindel, S., Becker, F., Rinderknecht, S.: Unbalance and resonance elimination with active bearings on a Jeffcott rotor. Mech. Syst. Signal Process. 85, 339–353 (2017)CrossRef

10.

Qiao, X., Hu, G.: Active control for multinode unbalanced vibration of flexible spindle rotor system with active magnetic bearing. Shock Vib. 12, 1–9 (2017)MathSciNet

11.

Saito, D., Waku, S.: Trial of applying unbalance vibration compensator to axial position of the rotor with active magnetic bearings. J. Jpn. Soc. Precis. Eng. 84(2), 210–208 (2018)CrossRef

12.

Cui, P.L., Zhao, G.Z., Fang, J.C., Li, H.T.: Adaptive control of unbalance vibration for magnetic bearings based on phase-shift notch filter within the whole frequency range. J. Vib. Shock 34(20), 16–20 (2015)

13.

Jiang, K., Zhu, C., Chen, L.: Unbalance compensation by recursive seeking unbalance mass position in active magnetic bearing-rotor system. IEEE Trans. Ind. Electron. 62(9), 5655–5664 (2015)CrossRef

14.

Paul, M., Hofmann, W., Steffani, H.F.: Compensation for unbalances with aid of neural networks. In: Proceedings of the Sixth International Symposium on Magnetic Bearings, pp. 693–701. Massachussetts Institute of Technology (MIT), Cambridge MA (1998)

15.

Lecun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)CrossRef

16.

Punjani, A., Abbeel, P.: Deep learning helicopter dynamics models. In: IEEE International Conference on Robotics and Automation, pp. 3223–3230. IEEE, Seattle (2015)

17.

Hung, J.Y.: Magnetic bearing control using fuzzy logic. IEEE Trans. Ind. Appl. 31(6), 1492–1497 (1995)CrossRef

18.

Dahl, G.E., Sainath, T.N., Hinton, G.E.: Improving deep neural networks for LVCSR using rectified linear units and dropout. In: IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 26, pp. 8609–8613. IEEE, Vancouver (2013)

19.

Sun, T., Pei, H., Pan, Y., Zhou, H., Zhang, C.: Neural network-based sliding mode adaptive control for robot manipulators. Neurocomputing 74(14–15), 2377–2384 (2011)CrossRef

20.