Data-Driven Systems Engineering · Long training periods Requires understanding of model and...
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Data-Driven Systems Engineering Turning MBSE into Industrial Reality SECESA 2018 – University of Strathclyde - Glasgow
Transcript of Data-Driven Systems Engineering · Long training periods Requires understanding of model and...
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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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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