Data Mining in MRO process optimisation · Data Mining in MRO process optimisation Maurice Pelt...

Data Mining in MRO process optimisation

Maurice Pelt

Aviation Academy,

Amsterdam University of Applied Science

[email protected]

RAeS Conference, London, 5 September 2017

Increasing Efficiency & Reducing Costs

within the Aircraft Maintenance Process

using New Technology and Innovative Solutions

mailto:[email protected]

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

2

Need for Data Mining in MRO process optimization

3

• 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

4

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

5

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

6

Too late

Too early

Right in

time

Right in

time and

known in

advance


Data understanding

Data preparation

ModellingEvaluation


First describe and analyse the past, then predict

the future and prescribe actions to be taken


Data understanding

Data preparation

ModellingEvaluation


• 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

9

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


Data understanding

Data preparation

ModellingEvaluation


MRO Costs break down

10

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


Data understanding

Data preparation

ModellingEvaluation


Form 1

3 main categories of data sources: Maintenance

data, FDR (AHM) and External data

11

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


Data understanding

Data preparation

ModellingEvaluation


Data preparation covers activities to construct the

final datasets from the initial raw data

12

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


Data understanding

Data preparation

ModellingEvaluation


Case Nayak : Causes of negative

performance in high season

13

CRISP methodology


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

14

Line maintenance and A checks, part of

operator TUI

Increase availability and lower

maintenance costs

CRISP methodology


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


Prediction: aircraft uptime ↑, part costs↓Not statistically significant (yet)

Case: Predictive maintenance model of legacy

aircraft using external data sources

15

CRISP methodology


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


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

16

Inflight data from aircraft engines are sent to

the manufacturer only

Improve maintenance efficiency using

free available data

CRISP methodology


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


Aircraft uptime ↑, Part costs↓EGT & LLP limits reached sooner than ERP

Case Jetsupport: Predict the duration of planned

maintenance checks

17

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


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


Aircraft uptime ↑, Maint. efficiency↑ More accurate planning of maintenance

Summary of 5 selected cases

18

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


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


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

19

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

20

Thank you for your attention

Maurice Pelt

[email protected]

co-authors:

• Robert Jan de Boer

• Jonno Broodbakker

mailto:[email protected]

Data Mining in MRO process optimisation · Data Mining in MRO process optimisation Maurice Pelt...

Documents

Transcript of Data Mining in MRO process optimisation · Data Mining in MRO process optimisation Maurice Pelt...