Data-Driven Systems Engineering · Long training periods Requires understanding of model and...

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Data-Driven Systems EngineeringTurning MBSE into Industrial Reality

SECESA 2018 – University of Strathclyde - Glasgow

Outline

Industrial reality

Data-driven SE

Implementation

Conclusion

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1. Industrial reality

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Video link:https://youtu.be/f-SP-PsAXeI

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Most companies in the space industry

still use a document-based approach to

engineering.

Photo created by Pressfoto - Freepik.com 5

86%of engineers’ time is spent on non-engineering work

100%*there are inconsistencies in the documentation

33%is spent on searching, reading and writing documentation

*extrapolation from personal experience 6

ConsequencesFailuresMars Climate Orbiter1999

Loss of spacecraft due to ground-based computer software which produced output in non-SI units of pound-force seconds (lbf·s) instead of the SI units of newton-seconds (N·s).

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2. Data-driven systems engineering

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Good data management is

becoming more and more essential for

engineering companies in the

space industry.

Tony Stark in Iron Man9

What about MBSE?

Identified problems

● Too complex and inflexible for practical use● Long training periods● Requires understanding of model and modeling

language● Much emphasis on the representation form of

models and overlooks the importance of the underlying data for the verification and analysis of models

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Data-Driven Systems Engineering (DDSE)

is a novel method, which enables a wider spread of MBSE throughout

the industry and allows for consistency of documentation

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DDSE

Our definition

A process where engineering data and associated

structure, links and connections constitute the

foundation of the systems engineering process.

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DDSE

Benefits

● Consistent database of connected engineeringvalues

● Automation● Traceability and transparency● Optimization possibilities

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3. Implementation

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To implement DDSE, it is necessary to start

by creating an infrastructure that

enables easy instrumentation and

data access for all stakeholders.

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The engineering data pyramid

Machine learning, Artificial Intelligence

Multidisciplinary design optimization

Budget, comparison chart, design structure matrix, history timeline

Search, filter, connection tracking, impact analysis, notifications

Self learn

Optimize

Aggregate / summarize

Explore

Structured storage

Collect / exchange

Component tree, matrices, margins, unit conversions

REST API, plugins and interfaces to specialized tools

*inspired by “The AI Hierarchy of Needs” by M. Rogati 16

Tool connections

To be truly data-driven, it is important to

connect tools and systems from different

disciplines and areas through open APIs.

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Browser-based tool stack

Benefits• Data exchange

through standard APIs

• Automated toolinteractions and connections

• Creates a singlesource of ’truth’

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4. Conclusion

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DDSE is a proposed solution that aims to enable model-based

engineering on a practical level for space companies

throughout the project lifecycle.

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Thank you!

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