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Machinery Prognostics and Condition Monitoring Technical
GroupDr. Karl Reichard
Machinery Prognostics & Condition Monitoring Technical Group
Applied Research LaboratoryPhone: (814) 863-7681 Email: [email protected]
Steve ConlonJeff BanksJason HinesJoe Rose
Cliff LissendonMarty TretheweyMitch LeboldJason Hines
Steve HambricJoe CusumanoJeff MayerBernie Tittman
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Wind Turbine Health Monitoring
Karl Reichard1, Eli Hughes1, Mark Turner1, Brenton Forshey2
1Applied Research Lab2Department of Aerospace Engineering
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Goals and Objectives
• Monitor health and status of individual wind turbines
• Facilitate CBM by predicting equipment failures and maintenance requirements
• Characterize wind-induced unsteady loads• Monitor health of generator, blades,
batteries, and power conversion and control electronics
• Optimize capacity of wind farm by aligning CBM with forecasted weather conditions
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Experimental Wind Turbine
• Instrumented Penn State Center for Sustainability’s Southwest Windpower Whisper 500, 3 kW, turbine
• Sensors:– 3 phase AC voltage and current– Converted DC power– Tower vibration– Local Meteorology– Blade vibration (planned)
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Wind Turbine Instrumentation
Wind
Blades Generator
Slip Ring
Tower
Electronics
Batteries
Met. Sensors
3-axis blade accel.
3-axis tower accel.
3-phase (AC) V&I3-axis gen vibe
Battery (DC) V&I
3-axis blade accel.
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Blade Health Monitoring
• Student team designed and is fabricating new blades for wind turbine
• New blades will contain embedded accelerometers to monitor blade vibration and unsteady loads
• Wireless monitoring system in hub will transmit vibration information to monitoring station at base of turbine.
• Future project – energy harvesting for wireless monitoring system
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• Triaxial accelerometer was installed 8 inches from the blade tip
• Cable will run to the hub where we will connect to a wireless transmitter
Accelerometer Installation
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System Design
Blades ElectricalGenerator Gearbox
Sensors
IntelligentSensorNode
IntelligentSensorNode
IntelligentSensorNode
IntelligentSensorNode
IntelligentSensorNode
IntelligentSensorNode
Sensors
SubsystemHealth
SubsystemHealth
SystemHealth
Operations
SubsystemHealth
Planning /Logistics
Knowledge
Data
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Wind Turbine Laboratory Testbed Suite
Blades Motor/ Generator Gearbox Electronics
Static & Dynamic Testing
Gear, Bearing and Shaft Testing
Generator electrical and mechanical
Power conversion and
switching
Energy Storage
Batteries
Monitoring System
Embedded Systems,
Processing, Data Fusion
Systems Integration
Lab
Enterprise Systems
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Wind Energy Enterprise Health Management
• Monitor health and status of individual wind turbines
• Facilitate CBM by predicting equipment failures and maintenance requirements
• Optimize capacity of wind farm by aligning CBM with forecasted weather conditions
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Jeff Banks, Mark Brought, Jason Estep, Jason Hines, Nathaniel Hobbs
Embedded Diagnostic and Predictive Technology Development for Platform
Power Generating Devices
Machinery Prognostics & Condition Monitoring Technical Group
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Objectives
• Demonstrate condition monitoring technologies on representative US Army tactical wheeled vehicle
• Vehicle Under Test: Freightliner 915 Truck
• Condition monitoring applied to detect alternator electrical (windings) and mechanical (bearing) faults
Machinery Prognostics & Condition Monitoring Technical Group
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Alternator
Machinery Prognostics & Condition Monitoring Technical Group
Stator
Rotor Assembly Rectifier Diodes in Housing
RegulatorHousing
Fan Blades
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Alternator Faults
Machinery Prognostics & Condition Monitoring Technical Group
Stator
Rotor Assembly Rectifier Diodes in Housing
RegulatorHousing
Fan Blades
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Alternator Instrumentation
Machinery Prognostics & Condition Monitoring Technical Group
CurrentSensor
Accelerometer
TemperatureSensor
MeasurementDescription
Sensor Model
Alternator Voltage LEM Model LV 20-PAlternator PhaseAB Voltage
LEM Model LV 20-P
Alternator PhaseBC Voltage
LEM Model LV 20-P
Alternator PhaseCA Voltage
LEM Model LV 20-P
Alternator FieldVoltage
LEM Model LV 20-P
Alternator Current LEM Model HAL200-S
Alternator FieldCurrent
LEM Model HAL 50-S
AlternatorTemperature
Standard K typeThermocouple
AlternatorVibration
PCB PiezotronicsModel 356M154
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Alternator Output Current
Machinery Prognostics & Condition Monitoring Technical Group
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
x 10-3
-0.5
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
Time (Seconds)
Amps
Time Domain Alternator Current Signature - 75 Amps - 100Hz Shaft Speed
Stator ShortNo Fault
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Alternator Output Current
Machinery Prognostics & Condition Monitoring Technical Group
0 2 4 6 8 10 12 14 16 18 20-140
-120
-100
-80
-60
-40
-20
0Power Spectral Density of Alternator Output Current
Frequency (kHz)
Pow
er S
pect
rum
Mag
nitu
de (d
B)
0 2 4 6 8 10 12 14 16 18 20-140
-120
-100
-80
-60
-40
-20
0Power Spectral Density of Alternator Output Current
Frequency (kHz)
Pow
er S
pect
rum
Mag
nitu
de (d
B)
No Fault
Stator Short
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Alternator Stator Short Fault Condition Indicator
Machinery Prognostics & Condition Monitoring Technical Group
0 1000 2000 3000 4000 5000 6000-100
-90
-80
-70
-60
-50
-40
-30
-20
-10
0
Frequency (Hz)
Pow
er S
pect
rum
Mag
nitu
de (d
B)
Power Spectral Density of Alternator Current
100Hz Alternator Shaft Speed150 Amp Load
1600Hz
3200Hz 4800Hz
600Hz
600Hz
600Hz
Comb filter used for fault detection
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Alternator Stator Short Fault Condition Indicator
Machinery Prognostics & Condition Monitoring Technical Group
0 1000 2000 3000 4000 5000 6000-100
-90
-80
-70
-60
-50
-40
-30
-20
-10
0
Frequency (Hz)
Pow
er S
pect
rum
Mag
nitu
de (d
B)
Power Spectral Density of Alternator Current
100Hz Alternator Shaft Speed150 Amp Load
1600Hz
3200Hz 4800Hz
600Hz
600Hz
600Hz
Comb filter used for fault detection
40 50 60 70 80 90 100 110
0
2
4
6
8
10
12
Alternator Shaft Speed
Con
ditio
n In
dica
tor M
agni
tude
Alternator Condition Indicator Response to Stator Winding Short
Stator Short - 75 AmpsStator Short - 50 AmpsStator Short - 25 AmpsNo Fault - 75 AmpsNo Fault - 50 AmpsNo Fault - 25 Amps
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Alternator Vibration Spectrum
Machinery Prognostics & Condition Monitoring Technical Group
0 0.2 0.4 0.6 0.8 1 1.2-90
-80
-70
-60
-50
-40
-30
-20
-10
0
Frequency (kHz)
Pow
er S
pect
rum
Mag
nitu
de (d
B)
Periodogram Power Spectral Density Estimate of Acceleration (Vertical)
Inner Race DefectNo Fault
1x ShaftSideband
538Hz
1x ShaftSideband
338HzInner Race Defect Frequency: 438Hz
2x ShaftSideband
238Hz
2x ShaftSideband
638Hz
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Conclusions
• Tested a variety of different faults on 140 Ampere Prestolite vehicle alternators for the Freightliner 915 truck.
• Alternators were then run under various electrical load and engine speed conditions to assess the detectability of faults utilizing different sensors and data collection systems
• Electrical fault of a single-phase stator winding short, and the induced mechanical fault of a defect on the bearing inner race were easily detected.
Machinery Prognostics & Condition Monitoring Technical Group
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Conclusions• The condition indicator algorithm developed for the single-
phase stator winding short looked at the variance in the time waveform of the sampled alternator current output– Simple and effective at indicating the presence of a seeded single
phase stator winding short at varying engine speeds and varying electrical loads.
– Can be implemented on existing fielded data analysis hardware that is currently being used on other vehicle systems in theater.
• A more robust algorithm based on the frequency analysis of the amplitude of harmonic components of the alternator signal was also developed. – Requires more computing horsepower, but is more sensitive to
earlier detection– Capable of giving a fault indication prior to failure that would allow
for condition based maintenance.Machinery Prognostics & Condition Monitoring Technical Group
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Mitch Lebold, Marty Trethewey, Nathan Lasut, Jonathan Bednar
Torsional Vibration Monitoring for Nuclear Power Plant Reactor Cooling
Pump Shaft Crack Monitoring
Machinery Prognostics & Condition Monitoring Technical Group
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Overview
• Multi-year project sponsored by EPRI• Uses torsional instead of lateral vibration
to monitor for cracks in RCP shaft• Prototype system developed and tested on
ARL test bed and on scale reactor test bed
Machinery Prognostics & Condition Monitoring Technical Group
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Experimental Test Bed
Machinery Prognostics & Condition Monitoring Technical Group
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Sensor Installation
Machinery Prognostics & Condition Monitoring Technical Group
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Torsional Vibration Spectrum
Machinery Prognostics & Condition Monitoring Technical Group
0 50 100 150 200 250
10-6
10-5
10-4
Frequency (Hz)
Degr
ees pe
ak
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Change in Torsional Vibration Frequency
Machinery Prognostics & Condition Monitoring Technical Group