data mining in mro process optimisation · data mining in mro process optimisation maurice pelt...
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Data Mining in MRO process optimisation
Maurice Pelt
Aviation Academy,
Amsterdam University of Applied Science
RAeS Conference, London, 5 September 2017
Increasing Efficiency & Reducing Costs
within the Aircraft Maintenance Process
using New Technology and Innovative Solutions
Contents
• Introduction
• Concept of Data Mining in MRO
• Results Business Understanding
• Results Data understanding and preparation
• Data Mining in MRO test cases
• Conclusions and Outlook
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Need for Data Mining in MRO process optimization
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• MRO: Unpredictable process times and material requirements
• Data Mining promises to improve predictability
• Focus on SMEs: Limited financial and data resources but important for our economy
• 2 year applied research project until Q3 2018: already 15 cases
Research question: How can SME MRO’s use fragmented historical maintenance data to decrease
maintenance costs and increase aircraft uptime?
Research aim
Data Mining in MRO
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Generic data mining recommendations for MRO
industry
Data mining solutions for specific MRO companies
Validation CRISP framework Knowledge development
Demonstration projectsNetwork and sharing
Aircraft uptime: Optimal and accurate MRO planning
Costs: Reduction over-processing and idle time
Costs: Optimal use remaining life parts
Toolbox for Data Mining in MRO
Data Mining models extract
information from monitoring data
PhysicalMathematics, degradation models
Knowledge basedDomain expert knowledge
Data drivenStatistics & learning(Un)supervised
HybridCombination of above
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ConditionSensors, data degradation monitoring
LoadForces, temperature, ..
degradation rate
UsageHours, cycles, kilometers
indication of degradation
External dataShared data
Environmental parameters
influences on degradation
Monitoring data Models to
extract information
Our focus
Business understanding
Data understanding
Data preparation
ModellingEvaluation
DeploymentCRISP phase
Strong growth in sensors
Strong growth in available data
Maintenance taxonomy
Maintenance
Reactive CorrectiveFailure based
Proactive
PreventiveSchedule based
Usage based
Condition based maintenance
Predictive maintenance
Model basedPhysical model
Knowledge model
Data driven
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Too late
Too early
Right in
time
Right in
time and
known in
advance
Business understanding
Data understanding
Data preparation
ModellingEvaluation
DeploymentCRISP phase
First describe and analyse the past, then predict
the future and prescribe actions to be taken
Business understanding
Data understanding
Data preparation
ModellingEvaluation
DeploymentCRISP phase
• Data mining: A sequence of steps
• Cross Industry Standard Process
for Data Mining methodology:
CRISP-DM
• Standard for data mining projects
based on practical, real-world
experience
• CRISP-DM is the most used data
mining method (Piatetsky, 2014)
CRISP-DM applicable for Data Mining in MRO ?
Source: Chapman, et al. (2000)
Identify the business drivers of a MRO company
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Total timeAircraft uptime
Aircraft Downtime
Backlog
Correctivemaintenance
Plannedmaintenance
Interval basedmaintenance
OEM
ReliabilityEngineering/AMP
Forecast Accuracy of Mx
Checks
Duration (Turn Around Time)
1. Identify performance indicators based on these drivers
2. Identify potential DM applications
3. Select relevant data sources
Aircraft Uptime break down
Business understanding
Data understanding
Data preparation
ModellingEvaluation
DeploymentCRISP phase
MRO Costs break down
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MRO costs
Materials
Per unit cost
Carrying costs
Labour costs
Interval of Mx
ReliabilityEngineering/AMP
Forecast Accuracy of Mx
Checks
Manhour per task
Inspections
Repairs
Component replacements
(rotables)
Manhour Buffer
Variance
Manhourestimate
Forecast accuracy
Nominal Taskload
Infrastructureand overhead
Business understanding
Data understanding
Data preparation
ModellingEvaluation
DeploymentCRISP phase
Form 1
3 main categories of data sources: Maintenance
data, FDR (AHM) and External data
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ERP
Jobcards
MPD
• Registration• ATA• Discrepancy• Corrective Action• Manhours• Engineer• Changed p/n, s/n• AMM, IPC reference• Date
• Vendor• P/N• S/N• Order Qty• SB status• Removal reason• Registration• Safety Stock lvl• Date stamps• Location (on + off a/c)
• P145 Release• TSN, TSO• P/N• S/N• Release
• Task• Skill• Interval• Time Since• Zone• Reference• Effectivity
FDRAHM
• Fault Codes• Actions• System parameters• Trends• Alert messages• Diagnostics• Date, fh’s, fc’s
ExternalData
• OEM databases• Wheater data• Aircraft position• Data of similar systems• Airport / runway data
Business understanding
Data understanding
Data preparation
ModellingEvaluation
DeploymentCRISP phase
Data preparation covers activities to construct the
final datasets from the initial raw data
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Cleaning steps Constructdata
Integratedata
Transformdata
Reducedata
Exsyn Remove duplicates; Remove false malfunctions Yes Yes Yes NoJetsupport 1 Remove errors; Fill empty cells; Remove empty cells;
Outliner removal; Remove irrelevant dataYes Yes Yes Yes
Jetsupport 2 Remove irrelevant data Yes Yes Yes NoJetsupport 3 Correct errors; Fill empty cells; Remove empty cells Yes No Yes NoLTLS - Yes No Yes YesNayak Correct errors; Fill empty cells; Outliner removal Yes Yes Yes NoRNLAF Remove errors; Fill empty cells; Remove irrelevant
dataYes Yes Yes No
Tec4Jets Remove errors; Fill empty cells; Remove empty cells Yes Yes Yes Yes
• Intial datasets based on business understanding
• Deal with imperfect and incomplete data
• Integrate, format and verify final data set
• Often tedious, time consuming
Business understanding
Data understanding
Data preparation
ModellingEvaluation
DeploymentCRISP phase
Case Nayak : Causes of negative
performance in high season
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CRISP methodology
Business understanding
Performance contract: aircraft uptimeCorrelate ATA (sub)chapter to problems
Data understanding
AMOS, weather data, flight data, unscheduled ground time events
Data preparation
Cleaned and integrated
Modelling Descriptive analysisSupport Vector Machine to predict problems related to weather
EvaluationDeployment
Aircraft uptime ↑, part costs↓Performance drop correlated to ATA subchapter, e.g. tyres, brakes and cabin air quality
A/B-checks and line maintenance for KLM
Fokker 70
Causes of drop in Fleet Availability
during high season
Case Tec4Jets: Optimal moment to change tyres
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Line maintenance and A checks, part of
operator TUI
Increase availability and lower
maintenance costs
CRISP methodology
Business understanding
Issue tree potential applications Selected: Prediction of wheel changes
Data understanding
AMOS, FDMcycles, weight, braking action, runway length and temperature
Data preparation
Cleaning, integration into single dataset
Modelling Visualise and calculate correlations
EvaluationDeployment
Prediction: aircraft uptime ↑, part costs↓Not statistically significant (yet)
Case: Predictive maintenance model of legacy
aircraft using external data sources
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CRISP methodology
Business understanding
Predict failures of components (ATA subchapters)
Data understanding
Maintenance data, ADS-B data (Flightradar24), weather data (NCEI)
Data preparation
Split in different flight phasesAveraging of parameters Dimensionality reduction
Modelling Clustering K-means detected 58 anomalies and DBSCAN 69
EvaluationDeployment
Aircraft uptime ↑, part costs↓Correlated failures and ADS-B dataShowed flight anomalies before component (nose wheel) failed
Access tot sensitive flight data is restricted
Reduce unplanned maintenance costs
excluding sensitive flight data and
replace this with other data sources
Case: Engine Health Monitoring
with data that are available for Airlines
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Inflight data from aircraft engines are sent to
the manufacturer only
Improve maintenance efficiency using
free available data
CRISP methodology
Business understanding
Economic Replacement Point (ERP), Life Limiting Parts (LLP) and Exhaust Gas Temperature (EGT) define the optimal replacement time of engines
Data understanding
Available data: EGT, fuel consumption, oil pressure and oil consumption
Data preparation Select engine typeClean and check data
Modelling Develop Engine Health Monitoring modelForecast optimal engine replacement point
EvaluationDeployment
Aircraft uptime ↑, Part costs↓EGT & LLP limits reached sooner than ERP
Case Jetsupport: Predict the duration of planned
maintenance checks
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0:00:00
12:00:00
24:00:00
36:00:00
48:00:00
MA
N-H
OU
RS
[H
R]
SCHEDULED PACKAGE
D E V I A T I O N A C T U A L V E R S U S I N D I C A T E D D U R A T I O N
Estimated Actual
JetSupport is CAMO of two Dornier aircraft of
the Dutch Coastguard
Increase availability with improved
planning of maintenance
CRISP methodology
Business understanding
Reduce uncertainty in: • Unplanned maintenance• Duration planned maintenance (findings)
Data understanding
MRX maintenance system
Data preparation Manual cleaning and integrationAutomated retrieval
Modelling Visualisation of planned actualForecasting algorithms based on actual duration of checkpackages and task cards
EvaluationDeployment
Aircraft uptime ↑, Maint. efficiency↑ More accurate planning of maintenance
Summary of 5 selected cases
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MRO industry recommendation
Solutions for MRO companies
Company Solution Contributes to
CRISP DM descriptive, predictive, hypothesis
Tec4Jets Predict tyre wear depending on destinations and other parameters
✓ Aircraft uptime✓ Part costs
CRISP DMpredictive, semi unstructured
Nayak Find components (ATA subchapters) contributing to low performance in high season
✓ Aircraft uptime✓ Part costs
CRISP DMdescriptive, predicive, hypothesis
Jetsupport Predict the duration of planned maintenance checks
✓ Aircraft uptime✓ Costs: MRO
utilisation rate
CRISP DM predictive, semi unstructuredparameter reductionno sensitive data needed
Exsyn Mx Predict maintenance needs using external data sourcesNose wheel failure as function of landing data
✓ Aircraft uptime✓ Part costs
CRISP DM predictive, hypothesisno detailed OEM data needed
Exsyn/ Engines
Predict optimal engine replacement time (EGT, LLP, ERP) with data that are available for Airlines
✓ Aircraft uptime✓ Part costs✓ Costs: MRO
utilisation rate
Conclusions
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Overall conclusions
Case studies proved the value of Data Mining• Aircraft uptime: optimal and accurate planning• MRO costs: efficiency, part costsCRISP-DM methodology useful for MRO
Business Understanding
Mostly problem (hypotheses) driven approachSupervised data driven approach also applicableAircraft uptime and MRO costs linked to data sourcesDistinct DM goals along MRO value chain
Data understanding
Data and business models not alignedData for compliance rather than predictionConfidentiality and ownership issuesSuccessful work arounds with own and public data
Data preparationData preparation much workNeed to improve data structures and capturing
ModellingDescriptive analyses very usefulPromising results with data driven approachFuture focus on predictive analyses
Source: MRO Air
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Thank you for your attention
Maurice Pelt
co-authors:
• Robert Jan de Boer
• Jonno Broodbakker