2020
DOI: 10.1007/s40997-020-00364-7
|View full text |Cite
|
Sign up to set email alerts
|

Development of a Novel Approach for Quantitative Estimation of Rotor Unbalance and Misalignment in a Rotor System Levitated by Active Magnetic Bearings

Abstract: In the present paper, a novel model-based identification algorithm is developed to estimate the unbalance and AMB misalignment in a rigid rotor system levitated with active magnetic bearings (AMBs). For this, a mathematical model for an unbalanced and misaligned rigid rotor system consisting of a rigid rotor levitated by two active magnetic bearings has been developed. Two cases of the misalignment depending upon the amount of radial offset between the rigid rotor and active magnetic bearings have been examine… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2021
2021
2023
2023

Publication Types

Select...
5
1

Relationship

0
6

Authors

Journals

citations
Cited by 13 publications
(1 citation statement)
references
References 29 publications
0
1
0
Order By: Relevance
“…Aggarwal et al [13] described the incremental inductance method to detect static misalignment of machines. Kumar et al [14,15] proposed a novel identification algorithm to simultaneously estimate the unbalance, misalignment, and active magnetic bearing stiffness parameters. Xu et al [16] studied the vibration response characteristics of a generator rotor under misalignment and stator short-circuit coupling fault.…”
Section: Introductionmentioning
confidence: 99%
“…Aggarwal et al [13] described the incremental inductance method to detect static misalignment of machines. Kumar et al [14,15] proposed a novel identification algorithm to simultaneously estimate the unbalance, misalignment, and active magnetic bearing stiffness parameters. Xu et al [16] studied the vibration response characteristics of a generator rotor under misalignment and stator short-circuit coupling fault.…”
Section: Introductionmentioning
confidence: 99%