literature presentation - health & performance monitoring using active magnetic bearing

1Challenge the future

AE4020 Literature StudyHealth & Performance Monitoring UsingActive Magnetic Bearing Systems


Contents

1. AMB System & Relevance

2. Advantages of using AMBs

3. Principle of operation

4. Objective

5. Remote monitoring using AMBs

6. Fault Classification

7. System modeling approaches

8. Cases to test

9. Additional Research

10. Deliverables

11. Timeline


1. AMB System & Relevance

Integration of

• Power Electronics

• Control Systems / Logic

• Rotor Dynamics

• Electromagnetic Technology

• Computing Technology


2. Advantages Of AMBs

• Application• No wear, noiseless, no lubrication, large range of

operational temperatures, elimination of path for circulating

currents.

• Control• Active control of imbalance & vibration, can vary stiffness

and damping as required, monitor system performance.

• Performance• Operate in critical environments, no lubrication related

problems, eliminates several components from system,

improved system reliability and longer operating life.


3. Principle of Operation


3. Principle of Operation (Contd.)


4. Objective

To create a health and performance monitoring tool whose deviation – reason matrix will be tested and established using a rotor dynamic model of the system.

Focus:•New key parameter indicators to capture misalignments

•To create a generic deviation – reason matrix for AMBs

•To understand the black box operation of AMBs using rotor dynamics and control – Eg: Machine Vibration


4. Objective (Contd.)

Series of Steps

1.Research various ways to develop a robust model – rotor dynamics perspective.

2.Failure mode analysis for AMB systems.

3.Data available and derivation of necessary parameters.

4.Build a deviation – reason matrix based on previous experience.


4. Objective (Contd.)

5. Build a rotor dynamic model.

6. Validate the model.

7. Test cases.

8. Add additional diagnostic capability.


5. Remote Monitoring using AMBs

Data Available From AMBs in SmartConnect:

• Current (AC, DC)• Rotor Position (Gap)• Coil Temperature• Power Amplifier Temperature• Rotor Unbalance• Flux• Voltage


6. Fault Classification

External Faults Internal Faults

Rotor impact Power electronics

Rotor mass loss Transducer malfunctions

Base motion Loss of I/O board channel

Rotor deformation Magnetic bearing coil failures

Sudden changes in loading Computer software errors

Rotor rub Computer hardware failures

Misalignment -


7. System Modeling Approaches

• Softwares – Ansys, ROMAC codes, Maple, Matlab / Simulink, FEMM

• Complexity• Rotor – Jeffcott, nDOF

• AMB – N, Ag, I, g, Permeability of medium

• Control Logic – PID, H-Infinity, Mu-synthesis, ADR etc.

• Control System – Centralized, Voltage vs Current

• Rotor / System Dynamics

• Validation


8. Cases to Test

• Coil Saturation Dynamics

• Stable Rotordynamics

• Unstable Rotordynamics – Continuous Disturbance

• Unstable Rotordynamics – Disturbance Outside Limits

• Rotor Levitation – Centering

• Effect of increased unbalance – Variation in Current


9. Additional Research

• Key aspects of specifying an AMB system• Design Considerations• Rotordynamic Considerations• Diagnostic Considerations

• Real time thrust force


10. Deliverables

• Creating new KPI’s – Early detection of incipient faults• Faster capture of machine health deviation

• Easier interpretation of data from the machine

• AMB supported rotor model to explain the “black-box” operation of AMBs.

• Experimentally backed Deviation – Reason Matrix

• How to specify an AMB system for first time users.


11. Timeline

• Modeling & Validation – First half of January• Model of Rotor, AMB with advanced controller• Validation with existing results

• Testing cases & result interpretation – Second half of January

• Report, consultation and defense – First half of February


THANK YOU

Questions Please!!


Fault Classification – Final List

Performance Radial Axial

Fouling Rotor Deformation Change in Vibration

Corrosion Change in Vibration

Coil Saturation

Impeller Damage Coil Saturation

Surge


Relationship Matrix - Faults

H – Upper limit violation of indicator value.

L – Lower limit violation of indicator value.

X – Upper or lower limit violation of indicator value.

FAILURE MODES

Loss of Performance

INDICATORS Fouling Corrosion Impeller Damage

Surge

Performanc

e / Process

Indicators

Head Deviation H - H H

Efficiency Deviation H H H H

Outlet Temp.

Deviation

H - H H

Polytropic Efficiency X L X X

General Parameters:



FAILURE MODESRotor Faults Radial

INDICATORS Rotor Deformation

Change in Vibration

Coil Saturation

Current X X H

Position X - X

Unbalance - H -

Temperature - - H

Radial Bearing Parameters:



FAILURE MODESRotor Faults Axial

INDICATORS Change in Vibration Coil Saturation

Current X H

Position - X

Temperature - H

Axial Bearing Parameters:


System Modeling

• UVa RotorLab• Rotor Modeling• M, D, K Matrix Extraction• AMB Supported Rotor option

• FEMM – Finite Element Method Magnetics: Core Electromagnet Design• Optimized AMB Design• Magnetic Flux analysis – Number of turns, cross section

shape, position of electromagnets, etc.

• COMSOL Multiphysics & MSC Software – ADAMS, PATRAN• Conflicting license issues


System Modeling

• Maple• Solving 2nd order differential equations

• MatLab / Simulink Approaches

• Pure MatLab

• Ansys + MatLab

• Matlab + Simulink

• Pure Simulink


System Modeling – Rotor

• Matrices from FEM softwares versus from papers

• More accurate rotor model, more DOF, larger matrix size (102 x 102), slower computation.

• FEM model matrix reduction as compared to simple assembly.

• Cross coupling between AMB system and rotor might not be captured.

• Validation concerns


System Modeling – Rotor

• Jeffcott Rotor vs Real Sized

• nDOF

• Mass matrix

• Stiffness matrix

• Damping matrix

• Gyroscopic matrix – When in Motion


System Modeling – AMB

• Depth of modeling

• Coils• Number of turns, resistance, inductance

• Cross-sectional area of the electromagnet in the gap

• Current supplied

• AMB Force relation – per electromagnet vs per pair vs per system


System Modeling – Control Logic

• Centralized vs Decentralized Control

• Voltage controlled vs Current Controlled

• Simple vs Complex



• PID

• PD control logic is simple and most widely applied

• It is better to omit the integral part since it destabilizes control logic in cases of sudden peaks or drops.

• Why then is PID preferred over a simple PD control for a AMB supported rotor model?

• Disadv – Lower frequency & higher nonlinear region



• Feedback Linearization

• Back-stepping approach

• Neural Network Control

• Fuzzy Logic Control

• Iterative Learning Control

• Automatic Learning Control



• H-Infinity Closed Loop Shaping

• H-Infinity Open Loop Shaping

• Q – Parameterization

• Mu – Synthesis

• ADR

• Sliding mode control



• Problems associated with an advanced control logic – a fine line

• Too much control

• Difficult to capture faults

• Adaptive nature might smother the possibility of knowing faults in the machine

• To detect faults or have a system that keeps running even when there are faults.


System Modeling – Rotordynamics

• Bode Plots• Frequency response of the system• Effect of changing damping on the system

• Impulse Response

• Transfer Function• Extracting transfer function from the state space

matrices of the system



• Poles

• Zeros

• Natural Frequency

• Resonant Frequency• Critical Speeds• Structural Resonance



• Damping Ratio

• DC Gain

• Nyquist Plot

• Eigen Values, Eigen Vectors

• Campbell Diagram


5. Series Of Steps

1. Research various ways to develop a robust model that can provide a good representation of the dynamics of the system from rotor dynamics point of view.

2. Research on the various failure mode analysis for a rotor on AMB system.

3. Get a good overview of the system from an operational point of view to understand what parameters are crucial in order to assess the health of the system.


5. Series Of Steps (Contd.)

4. Check for what data is available from an AMB system and how the required parameters can be derived from them. Current focus here involves extracting real time thrust measurements and vibration values using rotor properties, data sheets and real time incoming data. Both these parameters can be extracted by developing a relation map between available and design data.• For thrust bearing, a 3-way relation between thrust force, control

current and gap needs to be established.

• For vibration data, a relation will have to be built between current

and position. The interest here is not the vibration seen with the

increased control current but the vibration amplitudes of the rotor

when simply levitated but not controlled. The increasing vibration

trend can provide a good idea of fouling or rotor damage.


5. Series Of Steps (Contd.)

5. Build a cause and effect matrix where the failures are matched against the parameter values. This matrix will represent how certain parameter values going out of bound on the upper or lower limit can indicate the incipient faults in the system.

6. Build a rotordynamic model to explain the working of an AMB system along with crucial plots of orbits, Campbell diagram etc. This model will be used to open up the black box operation of the AMB system.

7. Test the matrix validity using case data from sites and the model.

8. Adding additional diagnostic capability by using an algorithm to check for sensor faults. This will be valid only for slow failures or faults in the sensors.


Case Studies

• Coil Saturation Dynamics

• Stable Rotordynamics

• Unstable Rotordynamics – Continuous Disturbance

• Unstable Rotordynamics – Disturbance Outside Limits

• Rotor Levitation

• Effect of increased unbalance – Variation in Current


Other Research – Thrust Force

• IMP\KPIs\Book1.xlsx

• IMP\THRUST CALCULATIONS\02.Design_and_analysis_of_thrust_active_magnetic_bearing.pdf

• Challenges • Available data• Validation• Accuracy


Other Research – User Note

• Key aspects of specifying an AMB

literature presentation - health & performance monitoring using active magnetic bearing

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