Using big data in aircraft maintenance – a practical guide on how data in aircraft maintenance can be used for predictive analytical purposes. Over recent years the topic of data being produced by aircraft has taken centre stage in the pursuit of enabling predictive analytical capabilities in aircraft maintenance. During this presentation we will dive into various aspects of data of aircraft, get familiar with some terminology and how data can be used to enable predictive analytics within maintenance & engineering. In addition, we will look at a real airline business use case that has applied the full methodology and how they have benefitted from this.

2. Sander de Bree, CEO

3. What to expect
1. About EXSYN
2. What is Predictive Maintenance
3. Why does it matter in Aviation
4. What do you need to get started
5. Case I: Component Failure
Prediction

4. About EXSYN

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EXSYN Aviation Solutions
• Amsterdam, the Netherlands
• Aviation Aircraft Data IT solutions provider
Founded in 2011 on the principle to:
‘Support airlines in adapting to an
increasingly digital aviation world’

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EXSYN Aviation Solutions
• 2016, named a top 20 company disrupting
Aviation by CIO solutions magazine
• 2017, 1st European Aviation company to be
certified by International Organisation for
Standardisation (ISO) on 27001 norm for data
security
• Today, running a cloud based Aircraft Data
Management platform for 1000+ aircraft
globally

7. AIRCRAFT DATA
MANAGEMENT
PLATFORM

8. Predictive
Maintenance

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What is Predictive Maintenance

10. Predictive Maintenance

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What is Predictive Maintenance

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The usage of statistical (mathematical) models to
continuously monitor the condition (serviceability of
assets) by determining the likeliness (n%) of an
occurrence happening in time (T1) with a known
dependency on variable(s) (X,Y,Z) that influence the
condition of the asset
Condition based maintenance enabled by statistical
probability of failure
What is Predictive Maintenance

13. Why does it
matter in
Aviation

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Pred. Maintenance in Aviation
• In recent years Pred.Maintenance has taken center stage for many
airlines & MRO’s
“Predictive analytics is one of the most prominent new technologies
(2020) according to airliners” [1]
• Main drivers for this industry attention:
• OEM driven by introduction of NextGen E-enabled aircraft
• Reduced costs of data storage
• Increased availability of data thru mobile and smart devices and
tools to access this data (IoT)
• Catching-up and spill-over from consumer market (social media
developments and consumer behavior analysis)
• Drive to reduce maintenance costs and more efficient use of
resource
[1] Canaday, H. (2013, February 11). New Predictive MRO Tools Cut Costs. Aviation Week & Space Technology, MRO Edition.

15. Pred. Maintenance in Aviation
“For the existance of
many possibilities, the
true purpose has been
forgotten”

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The real value
In order to understand the real value of predictive maintenance we
need to take a quick history tour:
1968, introduction of MSG philosophy on B747-100. Focussed on evaluation and adaption of
maintenance task effectiveness. Mx Cost reduction of 25% to 35% noticed by first B747 operators
1970, introduction of MSG-2. MSG is made availale for all aircraft types. Further reducing
maintenance costs for airlines
1979, introduction of MSG-3. Hard-time requirement & On-Condition Monitoring is introduced.
The birth of Reliability Management to serve as monitor for on-condition components and systems
Today, Aircraft Maintenance Programs still based on MSG-3 with Preventive inspections,
replacement and overhaul still being the norm

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The real value
Aircraft Maintenance Programs still based on MSG-3 with Preventive
inspections, replacement and overhaul still being the norm.
However:
• studies showed that on average only 18% of assets’ failure probability is
related to age (FH / FC)
• For the other 82% of the assets, the predictive maintenance
methodology is more suitable
In other words;
There is still a big potential to further reduce maintenance costs if we are able
to monitor the condition of the 82% and only replace / repair when condition
indicates as such

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Why now?
1. Technology and computing power available today
allows us to develop reliable statistical solutions for
condition based monitoring
2. Forecasted global shortage of Aircraft Engineers
(ICAO) requires to rethink the way we do aircraft
maintenance

19. What do you
need to get
started

20. Levels of data adoption - Process
P H A S E S
P
r
e
d
.
M
X
Reportive Airline
Producing monthly reliability
reports
• A/C utilization
• Removal history
• PIREP
• MAREP
• Spec2000 chptr 11
• Excel driven
Data driven Airline
Becoming Predictive
• Industry data
• AHM/ACMS sensor data
• FDR
• ADS-B transponder
• WX data
• Platform driven
Monitoring Airline
Continuous monitoring
• Same datasets as a
reportive airline
• Continues reliability
monitoring
• Monthly reporting
• Analytical software and
dashboard driven

21. Datamodel requirements
AOG risk prediction Comp Failure based
stock level prediction
Base MX non-routine
predictions
Avilytics Operator
Comparison
Aircraft/Fleet Utilization X X X x
Component removal records (US) X X X
Marep/Pirep X X X
NR findings X X X
Taskcard definitions / Maintenance Program X X
Inventory X X
Parts definition data X X X X
Supplier defintion data X X X
Materials Order data X X X
Flight Data Recorder X X
AHM readouts & other sensor data X X
Minimum Equipment List X X
* As based on EXSYN’s platform capabilities

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Datamodel requirements
In a 2017 study conducted by amongst airlines,
participants indicated that:
• Their number 1 focus is on Predictive Maintenance
• Their biggest issue faced is data accuracy

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Datamodel requirements

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Datamodel requirements
Things we need to think about as airline:
• With who do we want to share our data?
• Are OEM’s really the right partner

25. Case I:
Component
failure prediction

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The case
Background
• 120 aircraft in the fleet
• Route network with many overnight bases.
• 70% of the fleet remains overnight at non-homebase
station
Business problem
• Unexpected component failures cause AOG situations on
non-homebase stations and require significant recovery
time due to lack of spare parts and resource on non-
homebase stations
Solution
• Using predictive maintenance techniques to introduce
condition based monitoring of components and systems in
the airline MCC that indicate the risk of AOG on individual
aircraft

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The case
Result
• Enabled informed decision making within MCC /
OCC
• Predictive maintenance does not always mean
preventive replacement
• More dynamic process for tail assignments on day-
to-day bases
• Reduction in number of non-homebase AOG’s due
to technical reasons
Challenges:
• Sending serviceable components for repair
• Having engineers replace serviceable components

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To Summarize
• Predictive maintenance is about condition
monitored maintenance derived from statistical
probability of failure and sensor data
• Today, Aircraft Maintenance Programs are still
based on MSG-3 with Preventive inspections,
replacement and overhaul still being the norm
• Anticipated global shortage of aircraft engineers
requires to rethink how we do aircraft maintenance
• Technology allows us to create solutions that
effectively monitor the condition of aircraft
components and systems and only perform
maintenance when condition indicates necessity

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To Summarize 2
• It’s an industry push to a new maintenance
philosophy. MSG-4 ??
• Data required for such models is scattered amongst
many actors in the field (OEM / Airline / MRO) and
needs to be shared
• Sharing hurdles could be overcome thru
independent 3rd parties and enable the significant
cost saving that can be expected from Predictive
Maintenance
• Airlines can already start today to have the early-
win advantages, but need to focus on the accuracy
and availability of their data

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