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Model-Based Risk Assessment in
Multi-Disciplinary Systems Engineering
Euromicro Conference series on Software Engineering ...
Introduction
2
Multi-Disciplinary Engineering
 Multidisciplinary Domain
 Mechanical Engineering
 e.g., design, producti...
Introduction: Industry 4.0 and its principles
3
 Industry 4.0: computerization of
manufacturing. Driving principles
1. In...
Introduction: Engineering of Industrial Production Systems
4
 AutomationML (AML) standard for tool
data exchange
 AML do...
Introduction: Engineering of Industrial Production Systems
5
Mechanical/Eletrical/Software
Components Library
«cloned»
Pro...
Introduction: Engineering of Industrial Production Systems
6
 Risk: the probability of an
occurring event which can have ...
Introduction: Engineering of Industrial Production Systems
7
 Risk: the probability of an
occurring event which can have ...
Problem Description
8
Risk management is an error prone
and cumbersome task for industrial
production systems especially w...
Contribution: Model-Based Measurement Process for AML
9
Jacquet et al. proposed a generic measurement process model to cal...
Contribution: Model-Based Suite for AutomationML
10
Jacquet et al. proposed a generic measurement process model to calcula...
Contribution: Model-Based Measurement Process for AML
11
• Our objective
Risk assessment
 in multidisciplinary systems en...
Contribution: Model-Based Measurement Process for AML
12
 We need models for...
1. the production system,
2. links among ...
Contribution: Model-Based Measurement Process for AML
13
 We need models for...
1. the production system,
2. links among ...
Contribution: Model-Based Measurement Process for AML
14
 We need models for...
1. the production system,
2. links among ...
Contribution: Model-Based Measurement Process for AML
From 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 V...
Contribution: Model-Based Measurement Process for AML
 Queries are executed on the system models
 Example: Lab-size prod...
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
 M...
Conclusion
 We discussed foundations for risk assessment in multi-disciplinary
software and systems engineering projects ...
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Model-Based Risk Assessment in Multi-Disciplinary Systems Engineering

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Presentation at the Euromicro Conference series on Software Engineering and Advanced Applications (SEAA), 2015, on Model-Based Risk Assessment in Multi-Disciplinary Systems Engineering. For further information see: www.sysml4industry.org

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Model-Based Risk Assessment in Multi-Disciplinary Systems Engineering

  1. 1. Model-Based Risk Assessment in Multi-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
  2. 2. Introduction 2 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 mechanical engineering electrical engineering software engineering
  3. 3. Introduction: Industry 4.0 and its principles 3  Industry 4.0: computerization of manufacturing. 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 = domain = tool = doc overall system design mechanical engineering electrical engineering software engineering Industry 4.0
  4. 4. Introduction: Engineering of Industrial Production Systems 4  AutomationML (AML) standard for tool data exchange  AML docs are XML-based artifacts  AML as pivotal language: Tool- specific docs can be transformed in AML docs overall system design mechanical engineering electrical engineering software engineering Industry 4.0 = domain = tool = doc XML-based artifacts CAEX.xsd
  5. 5. Introduction: Engineering of Industrial Production Systems 5 Mechanical/Eletrical/Software Components 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.
  6. 6. Introduction: Engineering of Industrial Production Systems 6  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)
  7. 7. Introduction: Engineering of Industrial Production Systems 7  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)
  8. 8. Problem Description 8 Risk management is an error prone and cumbersome task for industrial production systems especially when having distributed models in different variants 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 overall system design mechanical engineering electrical engineering software engineering = doc
  9. 9. Contribution: Model-Based Measurement Process for AML 9 Jacquet et al. proposed a generic measurement process model to calculate metrics for software engineering projects.
  10. 10. Contribution: Model-Based Suite for AutomationML 10 Jacquet et al. proposed a generic measurement process model to calculate metrics for software engineering projects. We contextualized inputs/outputs for each step.
  11. 11. Contribution: Model-Based Measurement Process for AML 11 • 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
  12. 12. Contribution: Model-Based Measurement Process for AML 12  We need models for... 1. the production system, 2. links among heterogenous, versioned artifacts 3. the metrics definitions 4. metric results from Model-Based Co-Evolution of Production Systems and their Libraries with AutomationML Berardinelli, Biffl, Maetzler, Mayerhofer, Wimmer IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2015) AML Metamodel defines the concepts and their relationships for production system modeling. Implemented using Eclipse Ecore previous work
  13. 13. Contribution: Model-Based Measurement Process for AML 13  We need models for... 1. the production system, 2. links among heterogenous, versioned artifacts 3. the metrics definitions 4. metric results from Linking and Versioning Support for AutomationML: A Model-Driven Engineering Perspective Biffl, Maetzler, Wimmer, Lueder, Schmidt IEEE International Conference on Industrial Informatics (INDIN 2015) Linking Metamodel defines the concepts and their relationships for representing links among artifacts conforming to different metamodels. Implemented using Eclipse Ecore previous work
  14. 14. Contribution: Model-Based Measurement Process for AML 14  We need models for... 1. the production system, 2. links among heterogenous, versioned artifacts 3. the metrics definitions 4. metric results from Structured 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
  15. 15. Contribution: Model-Based Measurement Process for AML From textual specification (not a model in MDE)…
  16. 16. Contribution: Model-Based Measurement Process for AML …to SMM Model «represents»
  17. 17. Contribution: Model-Based Measurement Process for AML  Definition of the numerical assignment rules Example Groovy Code:
  18. 18. Contribution: Model-Based Measurement Process for AML  AML Models and Linking Models among different versions Different Versions of v1 v2 «links»
  19. 19. 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: 8 3. Max. Width: 10 4. 2378 : nodes 5. 2731 : edges 6. 1.148 : edgeToNodeRatio 7. Usage Metrics: 8. ***** Class Usage 9. 2 : FabrikModell/Bauteile/Motor 10. 2 : FabrikModell/Bauteile/Endlagenschalter 11. 1 : FabrikModell/Bauteile/Untergestell_Turntable 12. 1 : FabrikModell/Bauteile/Obergestell_Turntable 13. 1 : FabrikModell/Bauteile/Drehkranz 14. 1 : FabrikModell/Bauteile/Band_Turntable 15. ***** Role Usage 16. 2 : Modell RoleClassLib/Motor 17. 3 : Modell RoleClassLib/Sensor 18. 1 : Modell RoleClassLib/Untergestell_Turntable 19. 1 : Modell RoleClassLib/Obergestell_Turntable 20. 1 : AutomationMLBaseRole/Structure/ResourceStructure/MechanicalAssembly 21. 1 : Modell RoleClassLib/Band 22. 7 : Modell RoleClassLib/Kabel 23. ***** Interface Usage 24. 4 : ModellInterfaceClassLib/Verzahnung 25. 12 : ModellInterfaceClassLib/Verschraubung 26. 7 : ModellInterfaceClassLib/Stromanschlussbuchse 27. 3 : AutomationMLBaseInterface/ExternalDataConnector/COLLADAInterface 28. 7 : ModellInterfaceClassLib/PLCopenXMLInterface/LogicInterface 29. 6 : ModellInterfaceClassLib/COLLADAInterface 30. 4 : ModellInterfaceClassLib/Lagerung 31. 14 : ModellInterfaceClassLib/Stromanschlussstecker
  20. 20. Contribution: Model-Based Measurement Process for AML  Measurement Results are part of the SMM Model
  21. 21. 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/
  22. 22. 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 22

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