Bauhaus Luftfahrt Jahrbuch 2017

BauhausLuftfahrt

19

Arbeitsprinzip eines CPS

Physical world

Cyber-physical interaction

Cyber representation

Das CPS generiert aus systemrelevanten

Sensordaten, etwa zur Flottenüberwachung,

ausführbare Information zur Entscheidungsunterstützung

und optimierten Systemsteuerung,

z. B. zur Stand- und Wartungskostenreduktion.

Working principle of a CPS

A CPS promotes system-relevant sensory data,

such as for fleet monitoring, into actionable

information for decision support and optimised

system control, e. g. allowing for reduced downtime

and MRO costs.

Fleet of assets

(components /

(sub-)systems)

Operator,

maintenance

technician etc.

Data &

information

Time-stamped, e. g.,

• Degradation indicators

• Usage data

• Environmental data

• Maintenance logs

Direct corrective actions

to avoid failures

Maintenance scheduling,

spare parts ordering

Fleet

history

Digital twin

• Data management

• Orchestration of models &

smart analytics

• Generate insights, make

health predictions (RUL*)

Health information

• Optimal decision support

analytics

*RUL: Remaining Useful Life

CPS-Kosten-Nutzen-Analyse

Die Kosten-Nutzen-Analyse stützt sich auf die

Receiver-Operating-Characteristics-Kurve zur

Leistungsbewertung und -optimierung der Datenauswertealgorithmen

und zur Identifikation von

Business Cases mit Nettonutzen.

CPS Cost-benefit analysis

The receiver operating characteristics curve can

be directly linked with cost-benefit analysis for

performance assessment and optimisation of data

analysis algorithms and for identifying business

cases with net benefit.

Net relative benefit [%]

100

0

-100

-200

*

Cost-benefit analysis – example

Business case

No business

case

Cost-optimised operating point

Rare failure mode

Semifrequent failure mode

Frequent failure mode

0.0 0.2 0.4 0.6 0.8 1.0

Probability of false alarm

Probability of detection

1.0

0.8

0.6

0.4

0.2

Receiver operating characteristics curve

Perfect (no penalties, only benefit)

*

Better algorithm

performance

Random

Cost-optimised operating point

Rare failure mode

Semifrequent failure mode

Frequent failure mode

0.0 0.0 0.2 0.4 0.6 0.8 1.0

Probability of false alarm

* denotes the economically viable range of the probability of false alarm for a rare failure mode

Dr. Andreas Sizmann Head of Future Technologies and Ecology of Aviation, Knowledge Management

Innovation und Evolution von Zukunftstechnologien beruhen nach William Brian Arthur („The Nature of Technology“) auf

zwei Hauptfaktoren, der Kombination von Technologien und der Nutzbarmachung natürlicher Phänomene. Quanteninformatik

und Graphene stehen als EU-Flagship-Themen hoch auf der F&E-Agenda. Disruptives Potenzial verspricht auch die kombinatorische

Evolution, aktuell von physischen mit Informationstechnologien. Die Evolution von isolierten, statischen Lösungen hin zu vernetzten

und an die Situation anpassungsfähigen Technologien schafft Cyber-physische Systeme mit neuen Fähigkeiten für die Luftfahrt. Hier

sehen wir gute Chancen, grundlegend neue Zukunftsthemen zu identifizieren.

Innovation and the evolution of future technologies are based on two primary factors, according to William Brian Arthur

(“The Nature of Technology”), the combination of technologies and the harnessing of natural phenomena. Indeed, quantum

informatics and graphene are both high ranked F&E topics with EU flagship funding. Combinatorial evolution promises disruptive

potential as well, currently by combination of physical with information technologies. The evolution of isolated static solutions

towards connected, situation-aware, and adaptive technologies creates Cyber-Physical Systems with novel capabilities for aviation.

This is a promising territory for future technology scouting.

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