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Effectiveness of Condition-Based
Maintenance (CBM) in Army
Aviation
Presented by
Dr. Abdel Bayoumi, University of South Carolina
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Presentation Overview
Part 1: Introduction to CBM
General Theory
Army Aviation CBM
Available Data
Part 2: Science of CBM
Overview of a CBM Research Program
Part 3: Economics of CBMCost Benefits Overview
Considerations for Program Scaling
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GENERAL CBM THEORY
Part 1: Introduction to CBM
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Maintenance Theory
Mechanical systems
eventually breakdown
Component life follows
observable trendMaintenance includes
all activities to sustain
an operational state
Maintenance can have
large impact on costs
Time
Numberoffailures
Break-in Normal life Wear-out
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Schemes of Maintenance
Maintenance
Corrective
Event-drivenBreakdowns
Emergency
Repairs
Preventative
Time BasedPeriodic
Fixed intervals
Specific time
Usage BasedLoad & time
Condition BasedVibration monitoring
Tribology
Thermography
Ultrasonics
NDT
Improvement
Reliability-drivenModification
Redesign
Retrofit
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Usage Monitoring
Performance indicators
Deficient part replacements
Based on fatigue theory and statistics
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Methods of Condition Monitoring
Static
Surveys
Strain
Dynamic
Vibration
Ultrasonic
Active Wafer
Thermal
Temperature
Imaging
Tribology
LubricantAnalysis
Wear DebrisAnalysis
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Vibration Monitoring
Frequencies of interest:
Shaft rotation
Cage rotation
Ball spin
Inner race ball pass
Outer race ball pass
Gear meshSideband
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Ultrasonic Measurements
Improved Signal to Noise Ratio
AE
VM
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Thermal Measurements and Imaging
0 fp, 80 F 100 fp, 120 F
300 fp, 180 F 600 fp, 210 F
900 fp, 230 F 1200 fp, 250 F
Lubricant starvation:
17500 20000 22500 25000 27500 30000
Time [s]
100
150
200
250
300
350
Tempera
ture[F]
Thermo - TRGB ODBThermo - TRGB IDBThermo - TRGB GM
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Tribology
WearLubricant analysis
Lubricant dynamics
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Preliminary Diagnostics Modeling
150 F
300 F
100 300 Testing time [h]
216 hours 280 hours 296 hours
Tempe
rature,
Vibration,
Wear
Physical observations of wear:
Temperature trend with full grease
Temperature trend without grease
Vibration trend
Tooth wear trend
500200 400
248 hours 328 hours 344 hours 488 hours
A hypothetical scheme relating temperature, vibration and wear
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CBM IN ARMY AVIATION
Part 1: Introduction to CBM
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Army Nomenclature
Condition Monitoring (CM) Devices:
Health and Usage Monitoring System (HUMS)
Digital Source Collector (DSC)
Specific Product / Program Names:Vibration Monitoring and Enhancement Program (VMEP)
Vibration Monitoring Unit (VMU)
Modernized Signal Processing Unit (MSPU)
Health and Usage Management System (HUMS)Integrated Mechanical Diagnostic Health and UsageMonitoring System (IMD-HUMS)
Integrated Vehicle Health Monitoring System (IVHMS)
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HUMS on the AH-64
First generationVMU
Current device:
1209 MSPU
Monitors vibration ofimportant drive traincomponents
Rotor track and balance
Future technology
1239 SuperHUMS
Includes flight regimerecognition abilities
Image taken from the
AH-64 VMEP Crewmember Information Guide
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HUMS on the UH-60
First generation
VMU
IMD-HUMS / IVHMS
Rotor SmoothingDrive Train HealthMonitoring
Exceedance Monitoring
Structural HealthMonitoring
Engine HealthMonitoring
Tail RotorTachometer
GearboxTachometer
Main RotorTachometer
Main RotorTracker
CockpitVertical (A)
CockpitVertical (B)
Pilot HeelVertical
RightAccessoryGearbox
LeftAccessoryGearbox
Left InputGearbox
Right InputGearbox
Main Gearbox
HangerBearings (3)
Oil Cooler (2) IntermediateGearbox
Tail RotorGearbox
Left Engine
Right Engine
CabinAbsorber
Tail RotorTachometer
GearboxTachometer
Main RotorTachometer
Main RotorTracker
CockpitVertical (A)
CockpitVertical (B)
Pilot HeelVertical
RightAccessoryGearbox
LeftAccessoryGearbox
Left InputGearbox
Right InputGearbox
Main Gearbox
HangerBearings (3)
Oil Cooler (2) IntermediateGearbox
Tail RotorGearbox
Left Engine
Right Engine
CabinAbsorber
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Army Oil Analysis Program
Components vs. Available Analysis Methods
Possible Analysis Methods
Component O
il
G
rease
M
SPU
F
DA
T
emperature
T
BO(hr)
Main Transmission 4500
Nose Gearbox (x2) 4500
Auxiliary Power Unit 4500
Hydraulic System (x2) on-condition
Intermediate Gear Box 4500
Tail Rotor Gear Box 4500
Engine on-condition
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UNDERSTANDING THEAVAILABLE DATA
Part 1: Introduction to CBM
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Theoretical Framework of CM
DataCollection
Raw Data
SignalProcessing
TransformedData
FeatureExtraction
ConditionIndicators
FaultClassification
Diagnostics
ConditionEvaluation
Prognostics
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Raw and Transformed Data
Time and frequency domain vibration data
-100
-50
0
50
100
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5
Asynchronous Time DataUSC-64D-TR TB-0026
12/22/2009 17:30:03 | fpg101 | Tail SP | 17:30:00
VibrationMagnitude(Gs)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
0 5000 10000 15000 20000 25000
Spectral PlotUSC-64D-TR TB-0026
12/22/2009 17:30:03 | FPG101 | Tail SP | 17:40:58 | Survey FPG101 Tail SP AFD Spectrum
VibrationMagnitude(
g)
Frequency (Hz)
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Condition and Health Indicators
Condition Indicators (CIs)
0
2
4
6
8
10
12
14
16
CI Trend Across AircraftUSC-64D-TR Latest CI value for all times
SurveyFPG101TailSPShockPulseEnergyJK1(g)
Tail Number
TB-0001
TB-0002
TB-0003
TB-0004
TB-0005
TB-0006
TB-0007
TB-0008
TB-0009
TB-0010
TB-0011
TB-0012
TB-0013
TB-0014
TB-0015
TB-0016
TB-0017
TB-0018
TB-0019
TB-0020
TB-0021
TB-0022
TB-0023
TB-0024
TB-0025
TB-0026
TB-0027
TN-0001
TR-0017
TR-2222
TR-TEST
TR-TEST3
TR-TEST4
TR-TEST5
TR-TEST6
0
2
4
6
8
10
12
14
16
18
15 Thu
Oct 2009
22 Thu 1 Sun 8 Sun 15 Sun 22 Sun 1 Tue 8 Tue 15 Tue 22 Tue
CI Across TimeUSC-64D-TR:TB-0026 for all times
SurveyFPG101TailSPShockPulseEnergyJK1(g)
Calendar Time
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Diagnostics and Prognostics
Remaining Useful Life prediction
Detectable Range
Precursor
Condition
Time
Prognoses
Functional Failure
Faults Detected
RUL
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SSeennssoorrFFuussiioonn
Vibe 1
Vibe 2
Vibe 3 Mutual Information 1
Sensor n Feature n
SSeennssoorrss
Temp n Temperature Level n
AE n Emission Rate n
FFeeaattuurreess((CCoonnddii tt iioonnIInnddiiccaattoorrss))
Unbalance-Misalignment
Spall
Crack
Fault n
Improper Lubrication
Crack Initiation
FFaauull tt CCllaasssseess
Vibe n Mutual Information 2
HHeeaall tthhCCoonnddii tt iioonn
DDIIAAGGNNOOSSIISS
PPRROOGGNNOOSSIISS
FFeeaattuurreeMMaappppiinnggFFaauull tt //DDiiaaggnnoossiissCCllaassssii ffiieerrss
((SSVVMM,,VVoottiinngg))
. . . . . .
. . . . . .
. . . . . .
f
HHeeaall tthh//PPrrooggnnoossiissCCllaassssii ffiieerrss
((BBaayyeessiiaannIInnffeerreennccee,,NNNNTT))
Failure Mode
RULP
f
f
f
f
f
f
C
C
C
C
C
C
. . .
FFeeaattuurreeLLeevveell SSeennssoorrFFuussiioonn
Good Condition
Stable Condition
Failure ConditionShock Pulse Energy
Kurtosis
Diagnostics and Prognostics
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Usage Monitoring Data
Flight Regime Based Usage Monitoring:
Estimates component loads from maneuverrecognition and theoretical design loading
Directly Measured Usage Monitoring:Utilizes load sensors on various components foractual loading conditions (still in development)
Both systems estimate component lifethrough Fatigue Damage Fraction Calculationand Miners Rule
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TAMMS-A Data
Period: 1999 Present
Data sources:
ULLS-A (2408-12, 2408-13, and Document Control
Register), VMU/MSPU Database
Aircraft Models:
UH-60A, UH-60L, AH-64A, AH-64D, and CH-47D
Establishments and Environments:
ALARNG, MOARNG, PAARNG, SCARNG, TNARNG, ATTC,
FT Campbell, FT Rucker, Hunter AAF, Kosovo, Korea, Iraq
Flight Hours: Over 35,500 hours
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DISCUSSION BREAK
Part 1: Introduction to CBM
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EXAMPLE OF A FULL SCALERESEARCH SUPPORT PROGRAM
Part 2: Science of CBM
CBM R h @ USC
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CBM Research @ USC
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Research Overview
ComponentTesting
Vibration
Analysis
FaultCharacterization
Advanced SignalProcessing
Lubricant
Analysis
LubricantCondition
ComponentCondition
Data Mining
CI Evaluation
CI Creation
FundamentalResearch
Data Integration
Cost BenefitAnalysis
NaturalLanguage
Processing
Sensor Fusion
SensorSelection
AlgorithmDevelopment
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USC CBM Testing Facility
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Component Testing and Characterization
TRDT test stands at the USC CBM Research Center
Tail Drive
Shafts
Intermediate
Gearbox
Hanger
Bearings
Tail Pylon
Driveshaft
Tail Rotor
Gearbox
Absorption
Motor
Drive Motor
T il R t D i T i (TRDT)
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Tail Rotor Drive Train (TRDT)
Test Stand
AH-64 Apache Helicopter USC Test Stand
M i R t S h Pl t (MRSP)
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Main Rotor Swash Plate (MRSP)
Assembly Test Stand
AH-64 ApacheHelicopter
USC Test Stand
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Laboratory Support
USC Metrology, Machine Vision Facility, and CNC Machining Facility
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Fault Characterization
Condition Indicators
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AH-64 Tail Rotor Gearbox Experiment
Unexpected findings
Output seal leak thoughtonly to affect static mastbearings
Study proved no internalseal betweencompartments
Implications
Output seal is field
serviceableGearbox does not needremoval for this condition
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Advanced Signal Processing
#1-S4
R=6.678
#2-S8
R=6.735
#4-S6
R=6.667
#7-S5
R=7.022
Pre-run
Indication
of Failure
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Advanced Signal Processing
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Lubricant Analysis
Lubricant viscosity
characterization
Lubricant flowdynamics
Grease ejection
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Measured Changes in AH-64 Grease Viscosity
Rapid loss of
lubricant viscosity
with permanent
changesDifferent lubricant
might be considered
Grease ejection does
not affect gearboxoperation or safety
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Data Mining Classifiers
Beyond traditionalthreshold trees
Start with test standexperiments in which
repeated results wereobserved
Attempt to find casestudies from fleet data
Evaluate results withcross-validation andseparated training sets
CI1 CI2 CI3
Classifier
Fault Class
Membership
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Predicting Aircraft Tail Numbers
All Aircraft CIs
Correctly Classified
Instances1823 (85 %)
Incorrectly Classified
Instances
320 (15 %)
Kappa statistic 0.8472
Mean absolute error 0.0252
Root mean squared error 0.0996
Relative absolute error 70 %
Root relative squared
error74 %
Total Number of
Instances2143
Tail Rotor Gearbox CIs
Correctly Classified
Instances (Aircraft)43.3%
Total Number of Aircraft 54
Correctly Classified
Instances (Test Stand98.3%
Total Number of
Gearboxes5
Utilized a Random Forest classifier
with 30 decision tress with 8
randomly selected Condition
Indicators
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Evaluating Condition Indicators
Time period under studyshows varying use peraircraft
CI examined fordependencyrelationships
Principle componentanalysis found:
138 original CI functions
84 orthogonalcomponents
40% redundancy ininformation
Individual Aircraft (tail numbers not displayed)
NumberofAcquisition
s
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Cost Benefit Analysis
0
500
1000
1500
2000
25003000
3500
4000
2000 2001 2002 2003 2004 2005 2006 2007
USDollar
s
Year
Cumulative Costs of Maintenance Test Flight Hour per
Mission Flight Hour
VMU/MSPU Equipped Fleet Baseline
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Integration Challenges
Depiction of the four-stage integration process
Collected
SourceData
Single
RelationalDatabase
Metadata
EnhancedDatabase
Results
and CaseStudies
HUMS
Files
Sensor
Value
Indicator
FaultAction
Vehicle
Date
Vehicle Date
Component
Severity
Rarity
Importation Tagging
Sensor
Value
Indicator
Fault
Action
MMS
Database
Sensor
Value
Indicator
Fault
Action
Analysis
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Natural Language Processing
Examined fault
descriptions
Approximately follows
Poisson distributionAverage record length
is less than 8 words
Simple grammarstructure observed
Analysis of TAMMS-A Fault Records
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0 5 10 15 20 25 30 35
Number of Words per Record
Probability
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Sensor Selection
Number of paper in this text and the corresponding reference number
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Evaluated parameters 16 18 19 20 21 55 22 23 24 25 26 27 28 29 30 31 32 33 56 34 36 39 46Seeded fault 16 70
Natural fatigue 6 26
Field case studies (natural fatigue) 3 13
Low lubricant, lubricant contamination 5 22
Vibration RMS analysis 9 39
Wideband vibration spectrum analysis 8 35Advanced statistics of vibration signal 7 30
Enveloping of vibration signal 7 30
More than two CM techniques were used 3 13
Roller bearings 2 8.7
Ball bearings 22 96
Journal bearings 2 8.7
Thrust bearings 1 4.3
AE was concluded to be better 16 70VM was concluded to be better 5 22
VM & AE were concluded to be equal 4 17
Smallest seeded fault was detected by both VM & AE 7 30
Literature review comparing vibration monitoring with AE sensors
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Analysis
Control/Data Acquisition
System
Control Data
Torque Temp. AE VibrationSpeed
Measurement data correlation (a)
Relative comparison of predictive capabilities of
available and emerging measurement methods (b)
(a) (b)
Sensor and Data Collection EvaluationSensor evaluation and data fusion:
AE
Oil debris analysis
Vibration
ESA
Fault init iation Failure
Time
Temperature (improper lubrication, installation)
Tem erature
Vibration
Temperature
Oil Analysis
ESA
AE
],[)()(1
jiAtWjDm
i
i=
=
S d D t C ll ti E l ti
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Maximum vibration level over measured
frequency band and temperature plots over time
0
a[g],
T[F]
First noticeableteeth damage
350 [h]
170 F170 F170 F
270 F
20
40
0
Sensors and Data Collection Evaluation
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DISCUSSION BREAK
Part 2: Science of CBM
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COSTS AND BENEFITS OFIMPLEMENTATION
Part 3: Economics of CBM
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Costs and Scale of CBM Programs
Implementation
Hardware Labor Training
Optimization
DataCentralization
ScientificResearch
Required Optional
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Difficulties in FMECA Process
Predicting possible failure modes and their
relative probability of occurrence requires:
detailed records for established systems
complex modeling for new systems
Bottom-line costs of a particular failure mode
are difficult to asses due to:
complex interactions between subsystems
supply chain variables which cannot be predicted
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Maintenance of Wholly-Critical Systems
WC systems are those in which the functional
failure of any component results in a total
loss of the system
Often cost of individual components are
vastly less than the whole system
Most easily addressed with aggressive
scheduled inspections and replacements
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Special Characteristics of WC Systems
Failure Profile of aSingle Component
Cumulative Failure Probabilities ofMultiple Components in Series
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Justifying CBM Costs on WC Platforms
Costs Gains
System RiskMitigation
Saved Parts
Improved
Operations
Capital andUpkeep
FalsePositives
Extra Training
In certain platforms, thecost of a system loss farexceeds that of any of theother consideredparameters
Assume false positivesare less frequent thanaggressive schedulereplacements
Possible to justify CBM bycomparing CM upfrontcosts to system costs
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Justifying CBM Costs on WC Platforms
This simplification is possible by considering
the subset of failures which cause the greatest
loss, i.e., the whole system. For WC Platforms
it will include nearly all failures
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US Army Aviation CBM Approach
The cost ratio for a CM device on a US Armyhelicopter is approximately 1:200, or 0.5%
Possible to achieve ROI by considering asset riskmitigation, i.e., crash prevention
Perfect candidate for pre-installation CBMCIs and thresholds could be refined later
Between 1999 present more than 700 AH-64A/D had onboard monitoring systems installed
The next step: CI refinement
Slowed by conservative TBO schedules
P I l i CBA
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LEAKING (LIQUID) 39%
SEAL/GASKET BLOWN 10%
WORN EXCESSIVELY 10%
SCORED 9%
GROOVED 7%
BEYOND SPECIFIED TOLERANCE 6%
PITTED 4%
TRGB causes of removal Maintenance Test Flights
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
14.0%
16.0%
18.0%
20.0%
2000 2001 2002 2003 2004 2005 2006
Year
MSPU Equipped
Baseline
Unscheduled MaintenanceMaintenance Test Flights Preliminary
results show an 80% reduction in MTFs over a
8 year span.
Unscheduled Maintenance Incidences of
unscheduled maintenance as a percentage of
total maintenance actions have been reduced
significantly.
0
5001000
1500
2000
2500
3000
3500
4000
2000 2001 2002 2003 2004 2005 2006 2007
USDollars
Year
Cumulative Costs of Maintenance Test Flight Hour per
Mission Flight Hour
VMU/MSPU Equipped Fleet Baseline
Post-Implementation CBA
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Benefits from CBM Implementation
Tangible
Reduction in correctivemaintenance
Increased operational
readiness ratesIncreased supply chainefficiency
Reduced number ofinspections and test flights
Fewer unnecessary partreplacements
Asset risk mitigation
Intangible
Increased confidence and
morale from end users
Increased focus on mission
Requires revised
maintenance management
policies
Cost Benefits Model for
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Cost Benefits Model for
Basic Implementation
Time
Costso
f
Operation Maintenance Program Costs
CBM Technology Costs
Total Costs
Net
Savings
Break-
Even Point
Pre-CBM
CostL
evel
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Optimal Amount of Maintenance
Amount of Preventative Maintenance Performed
Co
stsofOper
ation
Preventative
Maintenance
Program Costs
Maintained
System Costs
Total Costs
Optimal
Maintenance Level
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COSTS AND BENEFITS OFOPTIMIZATION
Part 3: Economics of CBM
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Costs and Scale of CBM Programs
Implementation
Hardware Labor Training
Optimization
DataCentralization
ScientificResearch
Required Optional
Optimization:
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Optimization:
Continuous Improvement
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Extra Costs of Optimization
Data Centralization ApproachRequires large volumes of data to be moved fromdeployed systems to a central server
Costs include workstations, network infrastructure,
bandwidth, servers, contractorsScientific Testing Approach
Requires a well-planned testing program tomaximize benefits and careful consideration ofcomponents and fault modes to be studied
Costs include test stands, contracted hourly rates,test articles, planning overhead
Cost Benefits Model for
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Cost Benefits Model for
Data Centralization
C
ostsandG
ains
Cost of Data
Centralization
Amount of Data Moved and Stored
Net Gains through
Data Centralization
Break-Even
Point
Maximum
ROI Point
Scalabili ty RangeTechnological
Limits
D C li i Li i
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Data Centralization Limits
The transmission and storage of fleet-wide datais a significant cost for centralized CBMprograms
The benefits which can be attained from global
access and analysis are ultimately limited by thecapabilities of the technology
Intelligent transmission and storage programsare needed to ensure an economical data
programThis means prioritizing critical data and deletingredundant or non-useful information
Cost Benefits Model for
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Cost Benefits Model for
Scientific Research
CostsandG
ains
Costs of
Experiments
Number of Scientific Studies Performed
Net Gains from
Research Results
Break-Even
Point
Maximum
ROI Point
Technological
Limits
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Scientific Research Limits
Scientific research generally does not follow therules of economy of scale
Similar to data centralization and analysisefforts, CBM technology will always have
limitations which must be taken into accountAttempts to improve and optimize CBMalgorithms and devices should continue only aslong as it is profitable to do so
Identifying the point of diminishing returns ismore difficult since marginal costs are roughlyconstant
C bi d C t B fit M d l
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Combined Cost Benefits Model
Time
Costso
f
Operation
Maintenance Program Costs
Up-Front
Costs
Total Costs
Net
Savings
Break-Even
Point
R&D
Costs
Data Movement Costs
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DISCUSSION BREAK
Part 3: Economics of CBM
Di i T i
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Discussion Topics
Optimization Challenges
Keeping control over data
costs and management
How to select the rightdata to purge
Justifying scientific
research quantitatively
Identifying thetechnological limits of the
hardware
Economics
Success stories of CBM
showing costs savings
Costs of false positives vssaved parts
Challenges of post-
implementation CBAs
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Discussion Topics
CBM Theory Army CBM
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Discussion Topics
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Seeded Fault Component Testing
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Drive train control and data acquisition program
Seeded Fault Component Testing
Sensors and Data Collection Evaluation
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Analysis
Control/Data Acquisition
System
Control Data
Torque Temp. AE VibrationSpeed
Data acquisition and sensor system installed at the USC CBM
Research Center
Seeded Fault Component Testing
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Seeded Fault Component Testing
Broken and severely damaged teeth of TGB input gear,
after testing at USC CBM Research Center
Preliminary Diagnostics Modeling
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y g g
Gear mesh frequencies component amplitudes during thefinal four days of gearbox life (a)
Comparison of the amplitudes of the first and second gear
mesh frequency harmonics (b)
(a) (b)
ComponentAmplitude[g]
05
10
15
20
25
30
35
40
25-Jun 26-Jun 27-Jun 28-Jun 29-Jun 30-JunGear Mesh Amplitude [g]
05
10
15
20
25
30
35
40
0 5 10 15
Mesh Frequency Amplitude
Mesh Frequency 2nd Harmonic Amplitude6/25/2008 6/26/2008 6/27/2008 6/28/2008
GearMesh
2ndHarmoicAmplitude[g]
Preliminary Diagnostics Modeling
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TGB Sideband Index CI and vibration order trends over time
10
30
50
70
0 170 [h]
Orders
0
1
23
4
[g]
Preliminary iagnostics Modeling
Analysis Technique Exploration
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y q p
Spectrograms for a baseline shaft and shaft with
unbalanced load
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Algorithm Development
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Algorithm Development
Vibe 1
Vibe 2
Vibe 3 Mutual Information 1
Sensor n Feature n
SSeennssoorrss
Temp n Temperature Level n
AE n Emission Rate n
FFeeaattuurreess((CCoonnddii tt iioonnIInnddiiccaattoorrss))
Unbalance-Misalignment
Spall
Crack
Fault n
Improper Lubrication
Crack Initiation
FFaauull tt CCllaasssseess
Vibe n Mutual Information 2
HHeeaall tthhCCoonnddii tt iioonn
DDIIAAGGNNOOSSIISS
PPRROOGGNNOOSSIISS
FFeeaattuurreeMMaappppiinnggFFaauull tt //DDiiaaggnnoossiissCCllaassssii ffiieerrss
((SSVVMM,,VVoott iinngg))
. . . . . .
. . . . . .
. . . . . .
f
HHeeaall tthh//PPrrooggnnoossiissCCllaassssii ffiieerrss
((BBaayyeessiiaannIInnffeerreennccee,,NNNNTT))
Failure Mode
RULP
f
f
f
f
f
f
C
C
C
C
C
C
. . .
Good Condition
Stable Condition
Failure ConditionShock Pulse Energy
Kurtosis