IDGA Army Aviation Summit 06-28-10

8/12/2019 IDGA Army Aviation Summit 06-28-10

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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



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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


9/88

Ultrasonic Measurements

Improved Signal to Noise Ratio

AE

VM


10/88

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



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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



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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



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EXAMPLE OF A FULL SCALERESEARCH SUPPORT PROGRAM


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


Instances (Aircraft)43.3%

Total Number of Aircraft 54


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



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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


58/88

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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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



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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



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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



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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


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


79/88

Discussion Topics


80/88

Seeded Fault Component Testing


81/88

Drive train control and data acquisition program


Sensors and Data Collection Evaluation


82/88

Analysis

Control/Data Acquisition

System

Control Data

Torque Temp. AE VibrationSpeed

Data acquisition and sensor system installed at the USC CBM

Research Center



83/88


Broken and severely damaged teeth of TGB input gear,

after testing at USC CBM Research Center



84/88

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]



85/88

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


87/88

Algorithm Development


88/88

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

IDGA Army Aviation Summit 06-28-10

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Transcript of IDGA Army Aviation Summit 06-28-10