Data-Driven Self-sensing Technique for Active Magnetic Bearing

Author:

Yoo Seong Jong,Kim Sinyoung,Cho Kwang-Hwi,Ahn Hyeong-JoonORCID

Funder

Korea government

Soongil University

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Mechanical Engineering

Reference22 articles.

1. Kadlec, P., Gabrys, B., & Strandt, S. (2009). Data-driven soft sensors in the process industry. Computers and Chemical Engineering, 33, 795–814

2. Gu, H., Zhu, H., & Hua, Y. (2018). Soft Sensing Modeling of Magnetic Suspension Rotor Displacements Based on Continuous Hidden Markov Model. IEEE Transactions on Applied Superconductivity, 28(3), 1–5

3. Liu, T., Zhu, H., Wu, M., & Zhang. W. (2020). Rotor Displacement Self-Sensing Method for Six-Pole Radial Hybrid Magnetic Bearing Using Mixed-Kernel Fuzzy Support Vector Machine. IEEE Transactions on Applied Superconductivity, 30(4), 1–4

4. Kadlec, P., & Gabrys, B. (2009). Soft sensors: Where are we and what are the current and future challenges? IFAC Proceedings, 42(19), 572–577

5. Sun, Z., Zhao, J., Shi, Z., & Yu, S. (2014). Soft sensing of magnetic bearing system based on support vector regression and extended Kalman filter. Mechatronics, 24, 186–197

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