machinery prognostics and condition monitoring … · dr. karl reichard machinery ... • safety...
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1
Machinery Prognostics and
Condition Monitoring Technical
GroupDr. Karl Reichard
Machinery Prognostics & Condition Monitoring Technical Group
Applied Research Laboratory
Phone: (814) 863-7681
Email: kmr5@psu.edu
Steve Conlon
Jeff Banks
Jason Hines
Joe Rose
Cliff Lissendon
Marty Trethewey
Mitch Lebold
Jason Hines
Steve Hambric
Joe Cusumano
Jeff Mayer
Bernie Tittman
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• Safety – early work in helicopter HUMS
• Maintenance – use HUMS to enable condition based maintenance (CBM)
• Manning – reduce manning through CBM and PHM
• Life Cycle Cost – reduce total life cycle cost through savings in maintenance, manning, and sustainment
• Logistics – extend savings through the enterprise by leveraging CBM and PHM across fleets of assets
• Asset Capability Management – manage asset health by matching mission requirements to capability
• Autonomous Operation– enable autonomous and automated response to changing external and internal operating conditions
Drivers for Health
Management
Machinery Prognostics & Condition Monitoring Technical Group
3
Embedded Diagnostics and Prognostics
Technology Development for Helicopter Power
Systems
Jeff Banks, Jeff Mayer, Todd Batzel, Matt Poese
Machinery Prognostics & Condition Monitoring Technical Group
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Test Setup for generator functionality
Drive rated for 11 HP @ 18,000 Rpm
3 phase VFD
AC motor
Electrical System
TestBed
200A load bank for
electrical loading
Machinery Prognostics & Condition Monitoring Technical Group
Starter /
Generator
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Starter / Generator
Diagnostics
Focus has been on detecting sparking at brushes on the
surrogate starter/gen. Sparking increases wear rate of
brushes, which are a priority item for prognostics
heavy sparking moderate sparking light sparking
Test conditions:
• Imposed by rotation of brush assembly or removal of
interpole/compensation winding
• Field current measurements are performed under a
continuum of these operating conditions
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Starter / Generator
Diagnostics
Sparking Test Results
– Consistent increase in field current is observed as sparking
level increases
– True for sparking induced by brush assembly rotation and
removal of interpole / compensation winding
heavy sparkingmoderate sparkinglight sparking
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Power Inverter
Diagnostics
• Change in capacitance (tank phase) algorithm to allow identification to be done with loads within the tolerance of listed inverter specifications.
• Generated state space model for sensitivity calculations of the MOSFETs and current source inductor.
• Created algorithm to determine output tank parameters.
• Conducting long term inverter testing.
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Power Inverter
Diagnostics
T1
0
1
2
3
4R2
1mΩ C14.882uF
V115 V
Q16
IRF540
XSC1
A B C D
G
T
Q15
IRF540
U2A
4050BD_15V
C17
1uF
R36
100ΩR522kΩ
C2547nF
CR231N4148
L3
1nH
R8
100nΩ
L7
1mH
C4
1uF
R21
100ΩR2222kΩ
C547nFCR2
1N4148
U2B
4050BD_15V
V215 V
XFG1
U4A
LM339N
5
4
3
2
12
U4B
LM339N
7
6
3
1
12
R115kΩ
R95kΩ
V3
V4
Manual PWM Circuit
T3
1
01
234 5
D2DIODE_VIRTUAL
L127mH
MultiSim Simulation Test Circuit
Machinery Prognostics & Condition Monitoring Technical Group
-1.50E+02
-1.00E+02
-5.00E+01
0.00E+00
5.00E+01
1.00E+02
1.50E+02
0.0E+00 5.0E-04 1.0E-03 1.5E-03 2.0E-03 2.5E-03
-2.0E+00
0.0E+00
2.0E+00
4.0E+00
6.0E+00
8.0E+00
1.0E+01
1.2E+01
1.4E+01
1.6E+01
Output Q15 Gate Q16 Gate
-200
-150
-100
-50
0
50
100
150
200
-0.001 -0.0005 0 0.0005 0.001 0.0015 0.002 0.0025
-2
0
2
4
6
8
10
12
14
16
Output Q15 Gate Q16 Gate
Modeled Measured
Model-based
diagnostic and
prognostic approach
compares real-time
measurements to
model predictions
and historic data
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Health Monitoring of Wind
Turbines
Machinery Prognostics & Condition Monitoring Technical Group
Karl Reichard, Susan Stewart, Dennis McLaughlin
Brenton Forshey, Brian Wallace, Mark Turner, Nate Lasut, Scott Pflumm
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Wind Turbine
Monitoring
• Instrumented Penn State Center for
Sustainability‟s Southwest
Windpower Whisper 500, 3 kW,
turbine
• Sensors:
– 3 phase AC and converted DC power
– Tower and blade vibration
• Goals:
– Characterize wind-induced unsteady loads
– Monitor health of generator, blades,
batteries, and power conversion and
control electronics
Machinery Prognostics & Condition Monitoring Technical Group
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Blade Health
Monitoring
Machinery Prognostics & Condition Monitoring Technical Group
• 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
12Machinery Prognostics & Condition Monitoring Technical Group
Wind Energy Testbed
• Laboratory-based electrical generator test bed permits study
of internal generator, drive train, and power electronic faults.
• Bearing testing planned for summer 2010
13Machinery Prognostics & Condition Monitoring Technical Group
Wind Energy Goals
• 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
14
Technology Evaluation and
Integration for Heavy Tactical
Vehicles
Applied Research Laboratory
The Pennsylvania State University
PI: Brian Murphy / PE: Mark Brought
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On-Vehicle Sensor
Integration for Monitoring
and Diagnostics
Applied to existing vehicle data sources• Integrate Vehicle Computer System (VCS)
• Develop and integrate common CBM graphical user interface
• Open data sources: J1939, J1708
• Proprietary data sources: ADM diagnostic messages, ADM operational parameters
Applied to new sensors- Engine oil condition analysis - Fuel level
- Engine oil level - Fuel filter condition
- Transmission oil level - Tire pressure monitoring
- Coolant sensor level - Brake wear monitoring
- Hydraulic reservoir oil level
Applied to power system components• Alternator: Voltage, Current, and Temperature
• Battery: V, I, T, State of Charge, and State of Health
• Ultracapacitor: V, I, T, and SOC
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Need for Primary Power
Management
• Reliable engine starting after long term storage
– AGM Battery loss on vehicles aboard Pre-Pro Ships (USNS Pomeroy)
– War Reserve, National Guard, etc. with long periods of inactivity
• Higher total power needed for high electrical demands (e.g.
A/C, C4ISR, CREW, IED countermeasures, lighting)
• Longer operation during „silent watch‟
• Reduced logistics burden
• Lower lifecycle costs
• Simplified maintenance and diagnostics
Battery Graveyard, Kuwait
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Primary Power Management
System (PPMS)
A common vehicle power & energy architecture across platforms
• Employs a split energy storage
system to optimize energy delivery
Uses a hydraulically-driven
alternator /generator for high
electrical power drive & accessory
loads
Uses ultracapacitor
for vehicle starting
Accommodates variety of
batteries to meet mission
requirements
Provisions
DC–AC, DC-DC
Power Sources
Integrates power
management &
control
Evaluated the utility
of a planetary gear
starters Accepts modular external
charging sources (e.g. BB-
2590, Solar, etc).
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Primary Power Management
System Built & Tested
Scalable across TWV‟s
Ultracapacitor and
controller for hybrid
starting system
PPMS
CTIS, ATC, ABS
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PPMS Key Findings
Findings:
• Hybrid starting system proved functional
• Works with wide range of batteries
• Ultracapacitors can restart vehicle many 1000‟s of times
• Hydraulic powered alternator proved functional
• Ultracapacitor recharge control system proven using BB
2590, can also use BB390 NiMH, etc.
Impact:
• Life cycle cost reduction, reliability, improved performance
in „silent watch‟ runtime, modularity, applicability across
family of TWV‟s
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Convoy Collision Warning
System
As-Built Prototype System for Test & Evaluation
•GPS and inertial sensors on each vehicle
•Wireless communication between vehicles
•Use of Netbook PC‟s
Approach
•Share precise separation distance
between vehicles
•Combine separation data and rate of
closure to determine warning
•Present audible and visual driver alert
System Testing• 2 and 3-vehicle testing conducted at
Penn State test track
• 3-vehicle convoy testing conducted
on PLS‟s at Yuma Proving Ground
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Convoy Collision
Warning System
Vehicle 2Vehicle 3
Vehicle 1
Immediate End of Test- all vehicles braked to a stop
Vehicle 2 “overran” Vehicle 1
Vehicle 3 stopped with headway between Vehicle 2
Takeaways:
• Red gives proper indicator of unsafe headway distance
• Need to tailor and adjust thresholds to optimize system
performance and driver effectiveness
Click Here for Movie
May have to exit out of
slide show to run it
22
Automated Reporting
Information Displays
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Structural Health Monitoring
Machinery Prognostics & Condition Monitoring Technical Group
Cliff Lissendon
Engineering Science and Mechanics
2424
Application: Adhesively bonded joint integrity
Sponsor: NASA Aircraft Aging and Durability
Problem Statement: structural integrity relies on bonded joints, thus
characterization of joint strength is critical
Approach: ultrasonic guided waves interact with defects and depend
on elastic properties
Results: conversion of Lamb waves to interface waves
depends on dispersion curves and wave structures
Frequency (MHz)
An
gle
(D
eg
ree
)
Aluminum (2 mm) Dispersion curve - GMM and LLW
0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
5
10
15
20
25
30
35
40
2525
Application: Composite laminate delamination detection
Sponsor: AFOSR Structural Mechanics
Problem Statement: low velocity impacts and other insults cause
invisible delaminations that reduce the compressive strength
Approach: phased array of piezoelectric transducers can focus and
steer energy that reflects off defects
Results: energy skewing has been overcome in
anisotropic laminates and segmented annular array
transducers developed
2626
Application: Fatigue crack prognostics in built-up structures
Sponsor: Ben Franklin Center of Excellence in SHM
Problem Statement: effective SHM relies on detecting, locating, and
sizing fatigue cracks in complex joint structures
Approach: piezoelectric sensor array to size fatigue crack at fastener holes
in built-up joints; probabilistic fatigue crack growth model predicts RUL
Results: fatigue crack located, probabilistic model
implemented using Monte Carlo simulation
Ni= 50 cycles
= 0.05
Cycles
Pr
acr = 0.015 m
2727
Application: Detect and size corrosion in well casing
Sponsor: DOE Gas Storage Technology Consortium
Problem Statement: corrosion in steel casing for gas storage wells
increases risk
Approach: design an ultrasonic tool for sizing corrosion and cracks
using both circumferential and longitudinal guided waves
Results: circumferential waves appear to be effective
Damage Reflection
Transmitted Wave
Reflected Wave
T R
T/R
Reflected Wave
Transmitted Wave
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