Active Magnetic Bearings Stiffness and Damping Identification from Frequency Characteristics of Control System

Jin, Chaowu; Xu, Yuanping; Zhou, Jin; Cheng, Chonghu

doi:10.1155/2016/1067506

Cited by 20 publications

(14 citation statements)

References 14 publications

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“…A method with multi-frequency excitation for measurement of equivalent stiffness and damping of the active magnetic bearing rotor was presented [7]. From the unbalance response [8,9] and frequency characteristics [10], similar kinds of magnetic bearing parameter identification approaches were found. For ball bearing systems, the parameters were identified with simulated and experimental data [11].…”

Section: Introductionmentioning

confidence: 99%

An optimized bearing parameter identification approach from vibration response spectra

Mutra¹,

Srinivas²

2019

J. vibroeng.

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In the present work, an effective identification methodology bearing dynamic parameters using measured vibration responses at the bearing is proposed. The flexible rotor is analyzed by using finite element beam model with nonlinear hydrodynamic bearing forces due to floating ring bearing supports. The frequency domain responses at different operating speeds are initially obtained in both the lateral directions. The error function is formulated as an average difference in amplitudes of two lateral displacements at a bearing node with known reference signals over a frequency range. The design variables are the speed dependent direct and cross-coupled stiffness and damping parameters of the bearing. With the side constraints on the variables, the error is minimized by using a modified particle swarm optimization scheme. The accuracy of the approach is tested with noisy input signals.

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Section: Introductionmentioning

confidence: 99%

An optimized bearing parameter identification approach from vibration response spectra

Mutra¹,

Srinivas²

2019

J. vibroeng.

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“…A method with multi-frequency excitation for measurement of equivalent stiffness and damping of active magnetic bearing rotor was presented by Jiang et al [10]. Based on the unbalance response [11,12] and frequency characteristics [13], similar kinds of magnetic bearing parameter identification approaches were presented. Likewise for ball bearing systems, the parameters were identified with simulated and experimental data [14].…”

Section: Introductionmentioning

confidence: 99%

An Integrated Bearing Parameter Identification Approach Using A Nonlinear Optimisation Scheme

Mutra

Srinivas

2019

IJAME

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In system identification process, efficient global optimisation schemes play a vital role. In the present work, an effective optimisation-based identification methodology of bearing stiffness and damping coefficients using the bearing response data is proposed. The flexible rotor is initially analysed by finite element model with nonlinear bearing forces. The dynamic equations of rotor are solved to obtain the bearing responses in frequency domain at different operating speeds. The equivalent speed dependent parameters of stiffness and damping are then obtained by solving an error-based optimal formulation via modified particle-swarm optimisation scheme. The robustness of the approach is tested with added input noise. The approach is illustrated with the simulation results of rotor supported on full floating ring bearings and using an experimental data obtained from a rotor supported on two similar journal bearings. The error in identification is not exceeding five percent. The modified particle swarm optimisation is converging faster, and it is taking less computational time. The results are shown in the forms of graphs and tables.

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“…The robustness and performance of the proposed method were checked against several measurement errors. AMB was used as an exciter to generate the force response data which were further used to develop an algorithm to evaluate the AMB dynamic parameters, both theoretically and experimentally [13]. A methodology was suggested that utilizes the unbalance response of the AMB integrated rotor to estimate the AMB closed-loop characteristic parameters [14].…”

Section: Introductionmentioning

confidence: 99%

Dual flexible rotor system with active magnetic bearings for unbalance and coupling misalignment faults analysis

Kuppa

Lal

2019

Sādhanā

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Rotating machines are the backbone of the present industrial world. Early fault detection and conditioning of these machines are primary concern of the researchers associated in this field. There are various faults (assembly error, coupling misalignment, looseness, imbalance, rotor crack, etc.) that cause malfunction of rotating machinery. Imbalance is one of the oldest problem and still challenging to perfectly balance the rotor. Imbalance leads to another inherent fault, i.e., coupling misalignment, especially in dual rotor or rotor train system. Imbalance and misalignment cause excessive vibration in the system that tends to shatter failure of the critical components of rotating machinery. In this article, active magnetic bearings (AMBs) are utilized to suppress the excessive vibration generated due to imbalance and misalignment. To regulate the controlling current of AMB a proportional integral derivative (PID) feedback controller is employed. A quantification technique is suggested to evaluate the tuned AMB characteristics along with imbalance and coupling misalignment dynamic parameters. A finite element method (FEM) modelling with high-frequency reduction scheme is utilized to acquire reduced system equations of motion. There are two advantages of employing condensation scheme, first, it reduces the number of sensors required and second, only linear (practically measurable) degrees of freedom are present in equations of motion derived. A SIMULINK TM code is prepared to solve a reduced linear differential equation. The time series feedback signals (current and displacement) obtained are transformed into a frequency series utilizing Fast Fourier Transformation (FFT) and utilized in developed algorithm. To establish the accuracy and effectiveness of the methodology, the estimated parameters are evaluated under two different frequency bands against measurement and modelling error (5% variation in mass of the disc and bearing characteristic parameters).

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Active Magnetic Bearings Stiffness and Damping Identification from Frequency Characteristics of Control System

Cited by 20 publications

References 14 publications

An optimized bearing parameter identification approach from vibration response spectra

An optimized bearing parameter identification approach from vibration response spectra

An Integrated Bearing Parameter Identification Approach Using A Nonlinear Optimisation Scheme

Dual flexible rotor system with active magnetic bearings for unbalance and coupling misalignment faults analysis

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