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Erschienen in:

2019 | OriginalPaper | Buchkapitel

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

verfasst von : Xuan Yao, Zhaobo Chen, Yinghou Jiao

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

Verlag: Springer International Publishing

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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.

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Vorheriges Kapitel Dynamical Characteristic Analysis of Elastic Ring Squeeze Film Damper in Rotor System

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Titel: Unbalance Vibration Compensation Control Using Deep Network for Rotor System with Active Magnetic Bearings
verfasst von: Xuan Yao
Zhaobo Chen
Yinghou Jiao
Verlag: Springer International Publishing
Buch: Proceedings of the 10th International Conference on Rotor Dynamics – IFToMM
Print ISBN: 978-3-319-99261-7

Electronic ISBN: 978-3-319-99262-4

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-319-99262-4_6

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