Model-Based Risk Assessment in Multi-Disciplinary Systems Engineering

Model-Based Risk Assessment inMulti-Disciplinary Systems Engineering

Euromicro Conference series on Software Engineering and Advanced Applications (SEAA) 2015

Arndt Lueder, Nicole SchmidtStefan Biffl, Luca Berardinelli, Emanuel Maetzler, Manuel Wimmer

Introduction

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Multi-Disciplinary Engineering

Multidisciplinary Domain Mechanical Engineering e.g., design, production, and operation of

machinery (powered tools) Electrical Engineering e.g., design of complex power system and

electronic circuits Software Engineering e.g., design, implementation, testing,

validation of software for machinery• Heterogeneous document/tool landscape

Mechanical Engineering Matlab, CAD tools…

Electrical Engineering EPLAN

Software Engineering Programming IDEs, Modeling Tools…

= domain = tool = doc

overall system design

mechanicalengineering

electricalengineering

softwareengineering

Introduction: Industry 4.0 and its principles

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Industry 4.0: computerization ofmanufacturing. Driving principles

1. Interoperability among mechatronic systems (a.k.a. cyber physical systems CPS), humans and factories

2. Virtualization: a virtual copy of the factory with sensed data

3. Decentralization: the ability of CPSs to make decisions on their own

4. Real-Time Capability: monitoring, analysis, planning, execution

5. Service Orientation: OPC Unified Architecture (SOA)

6. Modularity: flexible adaptation to changing requirements





softwareengineering

Industry 4.0

Introduction: Engineering of Industrial Production Systems

4

AutomationML (AML) standard for tooldata exchange

AML docs are XML-based artifacts AML as pivotal language: Tool-

specific docs can be transformed in AML docs




softwareengineering

Industry 4.0


XML-basedartifacts

CAEX.xsd


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Mechanical/Eletrical/SoftwareComponents Library

«cloned»Production System Model

«represents»

Lab-sized Production System“Equipment Center for Distributed Systems,” http://www.iafbg.ovgu.de/en/technische ausstattung cvs.html, Institute of Ergonomics, Manufacturing Systems and Automation at Otto-v.-Guericke University Magdeburg.


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Risk: the probability of an occurring event which can have a negative impact on system overall quality

Risk Assessment: collection of adequate metrics to feed analysis processes including the identification of countermeasures throughout the system engineering process

Model-Based Risk Assessment: collection of metrics on (machine readable) models (e.g., AML ones)


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Risk: the probability of an occurring event which can have a negative impact on system overall quality

Risk Assessment: collection of adequate metrics to feed analysis processes including the identification of countermeasures throughout the system engineering process

Model-Based Risk Assessment: collection of metrics on (machine readable) models (e.g., AML ones)

Problem Description

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Risk management is an error prone and cumbersome task for industrial production systems especially whenhaving distributed models in differentvariants and versions

Needs for metrics for AML artifacts Needs for linking and versioning

support for AML artifacts Lack of Model-Based foundation for

risk assessment based on AML

= domain = tool




softwareengineering

= doc

Contribution: Model-Based Measurement Process for AML

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Jacquet et al. proposed a generic measurement process model to calculate metrics for software engineering projects.

Contribution: Model-Based Suite for AutomationML

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Jacquet et al. proposed a generic measurement process model to calculate metrics for software engineering projects. We contextualized inputs/outputs for each step.


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• Our objective

Risk assessment in multidisciplinary systems engineering projects based on AML and linked AML artefacts to reason on a set of distributed engineering artifacts

and their relationships

<<metamodel>>AttributedGraph

<<metamodel>>AMLMetamodel

<<model>>AMLModel

<<model>>AMLLibrary

conformsTo Relationship

Legend

<<metamodel>>LinkMetamodel

<<model>>LinkModel

connects Relationship


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We need models for...1. the production system, 2. links among heterogenous, versioned artifacts3. the metrics definitions4. metric results

fromModel-Based Co-Evolution of Production Systemsand their Libraries with AutomationMLBerardinelli, Biffl, Maetzler, Mayerhofer, Wimmer

IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2015)

AML Metamodeldefines the conceptsand their relationshipsfor production systemmodeling.

Implemented usingEclipse Ecore

previous work


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fromLinking and Versioning Support for AutomationML:A Model-Driven Engineering PerspectiveBiffl, Maetzler, Wimmer, Lueder, Schmidt

IEEE International Conference on Industrial Informatics(INDIN 2015)

Linking Metamodeldefines the concepts and their relationships for representing links amongartifacts conforming to different metamodels.

Implemented usingEclipse Ecore

previous work


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fromStructured Metrics Metamodel (SMM) by OMG.http://www.omg.org/spec/SMM/

“The SMM is a specification for the definition of measures and the representation of their measurement results. The measure definitions make up the library of measures and that serves to establish the specification upon which all of the measurements will be based.” from http://www.omg.org/spec/SMM/1.0/

Implemented using Eclipse Ecore

http://www.omg.org/spec/SMM/

http://www.omg.org/spec/SMM/1.0/

Contribution: Model-Based Measurement Process for AMLFrom textual specification (not a model in MDE)…

Contribution: Model-Based Measurement Process for AML…to SMM Model

«represents»

Contribution: Model-Based Measurement Process for AML Definition of the numerical assignment rules

Example Groovy Code:

Contribution: Model-Based Measurement Process for AML AML Models and Linking Models among different versions

Different Versions of

v1 v2

«links»

Contribution: Model-Based Measurement Process for AML Queries are executed on the system models

Example: Lab-size production system result:1. Length Metrics:2. Max. Depth: 83. Max. Width: 104. 2378 : nodes5. 2731 : edges6. 1.148 : edgeToNodeRatio7. Usage Metrics:8. ***** Class Usage9. 2 : FabrikModell/Bauteile/Motor10. 2 : FabrikModell/Bauteile/Endlagenschalter11. 1 : FabrikModell/Bauteile/Untergestell_Turntable12. 1 : FabrikModell/Bauteile/Obergestell_Turntable13. 1 : FabrikModell/Bauteile/Drehkranz14. 1 : FabrikModell/Bauteile/Band_Turntable15. ***** Role Usage16. 2 : Modell RoleClassLib/Motor17. 3 : Modell RoleClassLib/Sensor18. 1 : Modell RoleClassLib/Untergestell_Turntable19. 1 : Modell RoleClassLib/Obergestell_Turntable20. 1 : AutomationMLBaseRole/Structure/ResourceStructure/MechanicalAssembly21. 1 : Modell RoleClassLib/Band22. 7 : Modell RoleClassLib/Kabel23. ***** Interface Usage24. 4 : ModellInterfaceClassLib/Verzahnung25. 12 : ModellInterfaceClassLib/Verschraubung26. 7 : ModellInterfaceClassLib/Stromanschlussbuchse27. 3 : AutomationMLBaseInterface/ExternalDataConnector/COLLADAInterface28. 7 : ModellInterfaceClassLib/PLCopenXMLInterface/LogicInterface29. 6 : ModellInterfaceClassLib/COLLADAInterface30. 4 : ModellInterfaceClassLib/Lagerung31. 14 : ModellInterfaceClassLib/Stromanschlussstecker

Contribution: Model-Based Measurement Process for AML Measurement Results are part of the SMM Model

Contribution: Model-Based Measurement Process for AML Future work: exploitation of result, e.g., for impact analysis

May be done with reporting tools such as Eclipse Birt

Connector to EMF models required

http://www.eclipse.org/birt/

Conclusion

We discussed foundations for risk assessment in multi-disciplinary software and systems engineering projects and proposed a model-based metrics suite for AML models and their inter-model links.

We plan to extend the AutomationML metrics suite for several aspects Further metrics and queries for AML and link model artifacts Integrating dynamic aspects of AML model elements specifications through PLCopen

XML and state-machine like notations Visualization of results using Birt or graphical and textual modeling editors

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Science

Model-Based Risk Assessment in Multi-Disciplinary Systems Engineering