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Condition Monitoring of Motor-Driven SystemsNREL Wind Turbine Condition Monitoring Workshop – 8 October 2009

Mike NowakD&P Program ManagerEaton – Innovation Center

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Outline

• Overall methodology• Motor diagnostic and prognostic algorithms

• Rotor speed detection • Winding temperature estimation• Shaft misalignment• Broken rotor bar detection• Pump cavitation• Bearing fault detection

• Extensions to wind

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Overall Condition Monitoring Methodology

Only motor voltages and currents are used.

Inferential, nonintrusive, continuous!

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• Rotor Speed Detection (R)• Shaft Misalignment (R, B) • Broken Rotor Bar Detection (R, B)• Pump Cavitation (O)• Winding Temperature Estimate (S) • Bearing Fault Detection (B)• Shaft Torque Estimate (R)• Power Metering (O)• Motor Efficiency (O)• Stator Winding Insulation Faults (S) • Power Quality (O) • Harmonics Analysis Algorithms (O)

Motor CBM Algorithm Summary

Bearing (B) related: 41%

Stator (S) related: 37%

Other (O): 12%

Rotor (R) related: 10%

Motor Fault distribution

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1710

1720

1730

1740

1750

1760

1770

1780

1790

1800

1710 1720 1730 1740 1750 1760 1770 1780 1790 1800

Measured Speed (rpm)

Estim

ated

Spe

ed (r

pm)

Sensorless Rotor Speed Estimation

Estimation error less than 1%.

Motor current spectrum analysis

( ) ⎥⎦

⎤⎢⎣

⎡+

−+= wdseh n

psnkRff2/

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sehfRotor Slot Harmonics

Slip and rotor speeds rω→ Sensorless, low-cost, and accurate !

1 1.01 1.02 1.03 1.04 1.05 1.06 1.07 1.08 1.09 1.1-20

0

20

Line Currents

A

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Rotor Eccentricity and Misalignment Detection

RM

S o

f C

urre

nt (

mA

)

0 20 40 60 80 100 1200

0.5

1.0

1.5

Detection of Rotor Eccentricity

Detection of Shaft Misalignment

0

0.1

0.2

0.3

0 15sec 30sec 45sec 1deg

all components

w/o important sidebands

important sidebands only

Motor faults can be detected using only stator voltages and currents

Similar Motor Faults:• Air-gap eccentricities• Load torque oscillation• Rotor unbalances• Shaft misalignment• Broken rotor bar

Motor Line Currents

AFAF

MotorControlCenter

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Rotor Fault (Broken Rotor Bar) Detection

• Algorithm is based on advanced wavelets analysis

• Detect developing rotor fault (crack/broken bar, end ring)

Wavelets – time/frequency domain multi-resolution analysis

00.00020.00040.00060.00080.001

0.00120.00140.00160.00180.002

1 2 3 4 5 6 7 8 9 10 11 12

Wavelet level

Faul

t In

dex

Healthy1 broken bar3 broken bar5 broken bar

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

Healthy 1 BrokenBar

2 BrokenBars

3 BrokenBars

4 BrokenBars

Faul

t Ind

ex

100% Load75% Load50% Load

0 0.5 1 1.5 2 2.5 3 3.5 4-100

0

100

D9

7.8125Hz to 3.9063Hz

Time(Sec)

0 0.5 1 1.5 2 2.5 3 3.5 4-100

0

100

D10

3.9063Hz to 1.9531Hz0 0.5 1 1.5 2 2.5 3 3.5 4

-20

0

20

D11

1.9531Hz to 0.97656Hz0 0.5 1 1.5 2 2.5 3 3.5 4

-2

0

2

D12

0.97656Hz to 0.48828Hz0 0.5 1 1.5 2 2.5 3 3.5 4

-1

0

1

D13

0.48828Hz to 0.24414Hz0 0.5 1 1.5 2 2.5 3 3.5 4

-1

0

1

D14

0.24414Hz to 0.12207Hz0 0.5 1 1.5 2 2.5 3 3.5 4

-2

0

2

A14

'0 Hz to 0.12207Hz

Mag

nitu

de

Fs/2

D1D2D3D4

Frequency

S’=S

A4

A1

A3

Fs/4Fs/8Fs/16Fs/32

A2

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Pump Cavitation Detection• Fluid vaporizes when the

pressure becomes to low, forming cavities that violently collapse causing damage to the impeller

• When pump cavitation occurs, the spectral energy of the current in the “lower side-band” (LSB) and “upper side-band” (USB) increase accordingly.

• Detection of cavitation by monitoring average LSB and USB magnitude.

• For example:• LSB: 25-55 Hz• USB: 65-85 Hz

LSB USB

Fig.2: current spectrum w ith cavitation

0 20 40 60 80 100 120 140 160 18010-2

10-1

100

101LSB USB

1.0PU

fbase

f1 f2 f3 f4

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Winding Resistance/Temperature Estimation

DC is injected every 1 minutes for a short period (0.5 sec)

Estimation error less than 3%.

• 37% of motor failures are caused by stator insulation breakdown ⇒ thermal overloading is one of the major causes

• Signal Injection-Based RS (and Temperature) Estimation• Inject signal in the stator → RS estimate and Temperature estimate

→ Sensorless, low-cost, and accurate !

Soft-starter

Drive

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Bearing Fault Detection• Bearing faults account for over 40% of all induction motor faults.• Bearing faults classification

• Single-point defects— Characteristic fault frequencies

• Generalized roughness— More realistic of incipient faults— Difficult to detect for current monitoring

• Bearing Test SetupAccelerate bearing wearingprocess to <100 hours

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Bearing Fault Detection Algorithm using Noise-Cancellation

• Effective to detect incipient and developing bearing faults (before bearing breaks)

• Noise canceller adaptively eliminates correlated harmonic components, leaving uncorrelated frequency content as bearing fault index

• None of the following are required:

- Machine parameters- Bearing dimensions- Nameplate values- High-resolution spectra analyzer.

Measured as reference

Estimated

Set Alarm ! Motor Failure!

Fault Index

Vibration

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Extensions to Wind• Motor Current Signature Analysis technology may be possible for

induction generator CBM• Low cost complement to sensor-based CBM technologies

(vibration, fluid condition, etc)• Reliable – no sensors to fail!

Nonintrusive, continuous, inferential

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Technical Challenges of Wind Application• What is special about wind turbine CBM?

• Variable speed and variable torque load• Time-varying and unsteady loads (wind gust)• Relative low shaft speed

• Unsteady wind load blurs the diagnostic signatures matured for other applications. Most existing CBM methods cannot be directly applied due to dynamic load.

• Adaptation of motor current spectral analysis (MCSA) techniques towards time-varying or time-frequency representations holds promise

• Scalability – How well do these CBM methods developed for relatively small motors (< 200 HP) scale-up to the large turbine generators?

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Condition Monitoring of Motor-Driven Systems

Mike NowakD&P Program Manager

Eaton - Innovation Center

Questions?

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