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FORUM 2011

FORUM 2011. Issues for the Industrial Health Management System (APSYS). Sommaire. Introduction Major Issues for HMS in Industry : against « one shot » syndrom Technical Issues: Languages and Algorithms Methodological Model Making Issues : Model Making Process Semantic Management

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FORUM 2011

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  1. FORUM 2011 Issues for the Industrial Health Management System (APSYS)

  2. Sommaire • Introduction • Major Issues for HMS in Industry : against « one shot » syndrom • Technical Issues: Languages and Algorithms • Methodological Model Making Issues : Model Making Process • Semantic Management • Library Management / Knowledge Capture and Archiving • Model Driven Architectures : Information Equivalency - Translation • Business Model Equation • Model Configuration Management • Conclusion

  3. Introduction Academics and Industrial Point of View • Academics Point Of View • Rather interest in theoretical issues • Language / Algorithms / Technical aspects • Industrial concerns include • Previous ones • But far beyond everything concerning recurrent process and cost • But however should share same points on interest and searching

  4. Introduction Industrial focus for model making process in HMS applications • Technical Issues: Languages and Algorithms • Methodological Model Making Issues : Model Making Process • In every context whatever main objective is • At every step of the project • Semantic Management • What is the real semantic reference how to document it • How to control it ? • Model Configuration Management : how to have updated models ? • Library Management / Knowledge Capture and Archiving • Model Driven Architectures : make information circulate • Business Model Equation : industry per industry

  5. Technical / Theoretical / Scientifical Issues Languages for Reality Virtualization • How to classify them: SysML, StateMate, • How to characterize them ? Functional Analysis Languages / State Graphs / StaeFlows – Statecharts / Hybrids Languages • How to measure capability of expression ? Architecture – Static Block Diagram – Dynamic Behaviour – Nominal Behaviour – All Possible Degraded Behaviours - • How to adapt level of abstraction for • Top level safety property formalozation • Functional knowledge expresssion • Hardware / Software / Logical description • Discrete / Continüous Knowledgeexpression

  6. Technical / Theoretical / Scientifical Issues Algorithms : Simple Formulation / Sophisticated and Complex Resolutions • How to simulate the system in its « normal » « expected » operational modes • How to simulate the system in « all » possible degraded modes taking into account random events like failure modes • How to inverse the « arrow of time »: start from an image and go bacwards to possible system trajectories • How to deduce what have happened and changed in a system given an observed complex and combined situation , and how to hierarchize results of deductions ? • How to proove steady verification of a property ? • Independancy on external interactions • Independancy on time • …

  7. Methodological Model Making / Modeling Process In all conditions • Every time of the life cycle • Whatever top level objective is: Safety Analysis / Performance Based Engineering / HMS production / • For different cultural profiles • In different Engineering contexts • Multiples Industrial Partners • Subcontractors with « COTS » procurement

  8. SOW Step 1 Specification System Step 2 FunctionalDesign F2 F1 Step 3 PhysicalDefinition Soft Step 4 Manufacturing Hard Methodological Model Making / Modeling Process Top Down / Bottom Up / Mixed ?

  9. F2 F1 Methodological Model Making / Modeling Process Many Different points of Views to manage • Functional / Physical duality : • Depending on the level of progress of the project • Depending on the level of detail in the Work Breakdown Structure • Depending on the point of view to be developped: • Functional reference, • Physical reference.

  10. Methodological Model Making / Modeling Process In all conditions

  11. Semantic Management Semantic Implicite Reference Documentation Issue • A modelis a set of :concepts, • rules, representation and assumptions • To best describe or simulate behaviour of a physical system. • To make model means to identify: • Formal relations • Best describing dependency relations between inputs / outputs ; • And expressed in terms of • logical and mathematical expressions • Synchronized state automates • Differential Equations

  12. Semantic Management Semantic Control • Reusability • Readability • Modularity • Concision • Complexity relevancy • Sructuration

  13. Semantic Management Semantic Variability • One model strongly depends on the author who made the model: two models can be equivalent from certain points of view without being similar or identical • Modeling process necessarily requires to be selective and to sort signs / patterns / facts and figures from reality modelled

  14. Library Management / Knowledge Capture and Archiving How is Technological / Physical Knowledge to be captured and saved ? • Design knowledge of components, modules and equipements • Input and output • How it works • How it dysfunctions • How it reacts to mis interaction • Failure Mode knowledge • Physical description and characterization • Occurrence time distribution law • Quantitative Knowledge • Parameter values of quantitative laws • Ratio Mode  FORMAL ISSUES OF LIBRARY PRODUCTION AND MAINTENANCE

  15. Model Driven Architecture : make information circulate Use and Re use of industrial information • Connectors between different tools and information frameworks • Compliancy between meta models for different languages used in a company • Model translation rules Meta model Input SYSML Meta model Target ALTARICA transformation specification (XMI)

  16. Business Model Equation At least one different model for every domain of actvity • Aeronautics: MBSE / MBSA , HMS • Railway: PBE / PBA, HMS • Automotive : Safety, RAMS, HMS • Oil and Gaz: PBE / PBA, AM, Safety

  17. Business Model Equation Affordability • Identify : • The lowest breakdown level you need. • Keep as a « Black Box » (not decomposed) • The less critical elements which do not need to be decomposed, • Do not forget that the decomposition tree of a system • Depends on how the model will be processed, • Do manage • Consistency rules between different decomposition trees (as designed, as maintained, as manufactured…)

  18. Conclusions Many concurrent issues for industrial process… • Technical issue : are Model Based / Driven approaches feasable ? • Organizational : how is the technical and organizational framework to supportthe company and the company in these approaches ? • Cultural : how do users change their relation to work and knowledge ? • Time Dependant Process Sustainability : how do changeability and variability of data and products produce additional costs in the process ? • Virutualization of Know How , Knowledge and System produced : how far does it really cover real world  Most of them are known but few benefit from real research

  19. Avez-vous des questions à poser ? Do you have some questions ?

  20. Merci pour votre attention… Thank you for your attention…

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