cooperative management of digital twins a major enabler ... · diesar direction internationale de...

DIESAR Direction Internationale de l’Evaluation, de la Sécurité et des Affaires Réglementaires

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AFNeT Standardization Days 2017

Cooperative management of Digital TwinsA major enabler for an ecosystem of services in aeronautics

AFNeT Standardization Days 2017 - Paris

Thierry Chevalier

Chief Engineer Digital Product Development & Manufacturing

April 19 & 20, 2017


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AFNeT Standardization Days 2017 - ParisApril 19 & 20, 2017

Digital twin

Cooperative digital twin concept

Enabling technologies

Associated standardization needs


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Digital twin


Use a generic virtual model of a process, product or service

Make it specific through coupling it with the physical world updating it in “real-time” through sensors maintaining it as an up-to-date mirror of reality

Typically used for analysis of data and monitoring of the physical system to head off problems before they even occur, prevent downtime

to calibrate the model developed allowing better simulation/evaluation of future opportunities

Digital twins refer to computerized companions of physical assets

that can be used for various purposes. Digital twins use data

from sensors installed on physical objects

to represent their near real-time status, working condition or position.

But gathering data on the behaviour of a physical system without tracking it's configuration,is severely hampering the value of such analytics tailored maintenance,

And tracking configuration in the digital twin is also opening the way for other services spares, permit to fly…


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Digital twin configuration managementAn intrinsically cooperative task


Parts are produced by suppliers

Put together by major component assembly factories

Major components are fitted together in final assembly lines

Daily maintenance is operated by the airlines

Regular maintenance is operated by maintenance, repair and operations actors

Derogations can be granted by aircraft manufacturer…

Large numbers of actors need to cooperate to keep track of an aircraft configurationall along its lifecycle

cooperative management of the configuration is needed

And the same apply to the behavioral data by the way…


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Digital Twin





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Distributed & ubiquitous identification & traceability Virtual & physical parts


All parts uniquely signed & traced, along all lifecycle, across complete ecosystem

Mechanical

Systems

Software

Virtual parts

Physical parts

• Distributed

• Symmetric

• Transactional

• Safe

• Auditable

MD5 data

signature

DNA or THz imaging

markup used as signature

V1

V2

V3

• part signature• author / company• qualification meta-data+ link to part data if virtual /

link to virtual part versionif physical

CompanyA

V1

CompanyB

V2

CompanyC

V3

part dataversion 3


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Distributed & ubiquitous identification & traceability Virtual & physical configurations


Relevant configurations traced, along all lifecycle, across complete ecosystem

V1

V2

V3

V1

V2

V3

V1

V2

V3

Configurations referencing

parts

Component assembly

Green A/C

Head of Version

Digital Twin

Design solution

• configuration signature• author / company• qualification meta-data• link to related HoV blueprint• link to constituent physical

parts + specific information(e.g. concessions, repairs)

• configuration signature• author / company• qualification meta-data• link to related parts / components

• Distributed

• Symmetric

• Transactional

• Safe

• Auditable• configuration signature• author / company• qualification meta-data• link to related Green A/C blueprint

+ specific information(e.g. cabin)

Configuration here is a flat list of constituents, no data model involved at that stage


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Service ecosystem


Common free identification service, all kind of services then possible on top

Secure & traceable

supply chain

Getting MSN

flight history

Getting spare parts

Getting part

3D model Getting maintenance

documentation

Up to date

MSN Digital Twin

Maintenance audit


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Digital twin




• Distributed

• Symmetric

• Transactional

• Safe

• Auditable


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Distributed Digital ThreadSpeed-up & improve reactivity along all the lifecycle


Building digital continuity between existing segmented ecosystems Across lifecycle : Product development > Manufacturing > Service > Recycling

Across all actors : in extended enterprise

Harvest real events to allow direct & lateral analytics feedback

Advanced human-machine communication Collect elaborated user profiles

to master complexity through personalized view on the data(Geometrics copyright )

leveraging 4 technologies:

• Semantic tagging of the underlying data,

to allow dynamic creation of global data models on demand

• Linked data to fetch more information directly from ecosystem

based on it’s Digital Thread indexed meta-data

• Big data to warehouse & resolve the semantic mapping

in a distributed & scalable way

• Blockchain to secure distributed / transactional sidentification

+ blockchain wallet to secure configuration management

• Deep learning for advanced communication models


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Supply chain track & traceSecure supply chain and enable a robust related services ecosystem


Physical marking of parts Embedded in the physical part (e.g. DNA like marking)

Passive & easily readable (e.g. RFID like)

Trace-ability of physical parts Distributed ledger allowing collective history tracing of all marked parts

Connexion to associated virtual parts (and to Digital Thread)

Digital Twins Distributed ledger maintaining bill of parts per MSN

+ specificities vs Virtual A/C blueprint (as is in Digital Thread)


• Embedded tagging of a physical part

• Blockchain to secure distributed symmetric identification

+ blockchain wallet to secure Digital twins configuration mgt

https://github.com/openblockchain/obc-docs/blob/master/images/Canonical-Use-Cases_Manufacturing-Supply-Chain.png

https://github.com/openblockchain/obc-docs/blob/master/images/Canonical-Use-Cases_Manufacturing-Supply-Chain.png


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Deep learning visionMaster complexity


Self-learning / self-improving systems (e.g. AlphaGo)

Autonomous learning

Easy to teach systems (e.g. Watson Jeopardy, Nvidia Drive)

Fast-track learning without modeling

Traceable inference from cured database of facts (e.g. WolframAlpha)

Mixing validated data & models

Easier to update / re-use blocks

Mix with laws


• Machine learning for classical classification approach

• Deep learning to move from analytic to advanced

predictive & prescriptive


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Digital twin





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Standardizing technology stack servicesBuilding sustainable services in a quickly changing ecosystem


Several new & rapidly evolving technologies still have largely immature / specific stacks Blockchain / Smart contracts

Deep learning

Need for standardized stacks / components / services to grow in a sustainable way Develop focalized skills

Re-use

Maintain


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Standardizing aeronautic ontologyBuilding an aeronautic “FoaF” ontology


Semantic interconnexion greatly facilitated if you can map concepts on a common ontologye.g. FOAF Allow each partner to map toward common ontology,

thus reducing the threshold for remaining mapping when interconnexion is needed

Some initiatives already going in that direction e.g. CPACS limited, quite focused on definition and missing most behaviors


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Thank you


cooperative management of digital twins a major enabler ... · diesar direction internationale de...

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