Semantic Web for Advanced Engineering
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Transcript of Semantic Web for Advanced Engineering
Semantic Web for Advanced Engineering
Marta SabouVienna University of Technology,
Institute of Software Technology and Interactive Systems, Christian Doppler Laboratory for „Software Engineering Integration for Flexible Automation Systems“ (CDL-Flex)
2Dimensions not to scale. Adapted from Stefan Biffl.
Semantic Web
Software Engineering
AutomationEngineering
MechanicalEngineering
ElectricalEngineering
MechatronicEngineering
Business Informatics
CDL-Flex
My Research Universe
Human Computation
Content
What is the Fourth Industrial Revolution?– What are scenarios where Semantic Web technologies could be used?
To what extent can Semantic Web (SW) technologies be used to support the scenario of multi-disciplinary engineering?
What are challenges of applying SW technologies?
The Fourth Industrial Revolution
Source: Forschungsunion Wirtschaft und Wissenschaft, Acatech,”Securing the future of German manufacturing industry. Recommendations for implementing the strategic initiative INDUSTRIE 4.0 .Final report of the Industrie 4.0. Working Group.”, 2013
Cyber-Physical Systems (CPS)
Flexible, adaptive manufacturing (CPPS)Smart, distributed transportation systems
Estimated Economic Impact
Potential boost to the European Union’s gross domestic product by €110 billion annually over the next five years.
Manufacturing
70% of global trade In EU:
– 2 million businesses– 34 million jobs– 60% of EU economic growth
(Some) Challenges:– Shorter time to market– Increased product diversification and customization – Highly flexibilized (mass-) production– Higher product quality– Improved efficiency
Towards Flexible Production Systems and Processes
Source: Forschungsunion Wirtschaft und Wissenschaft, Acatech,”Securing the future of German manufacturing industry. Recommendations for implementing the strategic initiative INDUSTRIE 4.0. Final report of the Industrie 4.0. Working Group.”, 2013
Production System: Product-Process-Resource
Production step: Slicing
Material: Body with slices
Production resource:Slicing robot
Material: Breadbody
Production step: Baking
Product: Bread
Production resource: Oven
Characteristics of modern, flexible production systems
plug-and-participate capabilities of production resources – the integration and use of new or changed production resources
during production system use without any changes within the rest of the production system
self-* capabilities of production resources – self-programming of production process control, self-maintenance in
case of technical failures, or self-monitoring for quality late freeze of product-related production system behaviour
– fixing the characteristics of an ordered product at the latest possible point before production step execution, e.g., enabling to change the ordered colour of a car until the start of painting
Engineering Phase
Business Requirements
Cyber Physical Production System (CPPS)
Integrate Business Requirements in Engineering
Deploy created artifacts
Production TransportSales
Process Eng.
Electrical Eng.
CAD, Pipe & Instrumentation
Electrical Plan
Tool Data
Tool Data
Customer Representative
Software Eng.
Customer Reqs. & Review
Tool Data
Software Dev. EnvironmentTool Data
Control Eng.
PLC programTool Data
Project Manager
Engineering Cockpit
PLC
Test/Operation Phase
Operator
SCADATool Data
Multi-Model DashboardTool Data
Diagnosis Analysis
Tool Data
OPC UA ServerConfig
ERP SystemTool Data
Production Planning
Tool Data
Business Manager
Production Manager
Control Eng.
PLC programTool Data
Cyber Physical Production System (CPPS)
Access runtime information
Access engineering information
Production TransportSales
Engineering Cockpit
OPC UA Server (augmented)
Business Manager
Enrich runtime information
1
2
3
4
Scenario 1:Engineering Tool Network
Scenario 2:Multi-disciplinary
Reuse
Scenario 3:Flexible
Production
Scenario 4:Maintenance
Support
Production System Life-Cycle
Sc1: Discipline-crossing Engineering Tool Networks - fault free information propagation and reuse in engineering networks covering different engineering disciplines, engineers, and engineering tools during the creation of a production system.
Sc2: Use of existing Artifacts for Plant Engineering - identification and selection of reusable production system components.
Engineering Phase
Business Requirements
Cyber Physical Production System (CPPS)
Integrate Business Requirements in Engineering
Deploy created artifacts
Production TransportSales
Process Eng.
Electrical Eng.
CAD, Pipe & Instrumentation
Electrical Plan
Tool Data
Tool Data
Customer Representative
Software Eng.
Customer Reqs. & Review
Tool Data
Software Dev. EnvironmentTool Data
Control Eng.
PLC programTool Data
Project Manager
Engineering Cockpit
PLC
Test/Operation Phase
Operator
SCADATool Data
Multi-Model DashboardTool Data
Diagnosis Analysis
Tool Data
OPC UA ServerConfig
ERP SystemTool Data
Production Planning
Tool Data
Business Manager
Production Manager
Control Eng.
PLC programTool Data
Cyber Physical Production System (CPPS)
Access runtime information
Access engineering information
Production TransportSales
Engineering Cockpit
OPC UA Server (augmented)
Business Manager
Enrich runtime information
1
2
3
4
Scenario 1:Engineering Tool Network
Scenario 2:Multi-disciplinary
Reuse
Scenario 3:Flexible
Production
Scenario 4:Maintenance
Support
Production System Life-Cycle
Sc3: Flexible Production System Organization – aims at run-time flexibility of production systems. Enables the integration of advanced knowledge about the production system and the product within the production system control at production system run-time.
Sc4: Maintenance and Replacement Engineering – combines engineering and run-time information of a production system towards improved maintenance capabilities of production system components.
Semantic Needs for Industrie4.0 Scenarios
Production System Engineering Needs & Scenarios SC1 SC2 SC3 SC4
N1 Explicit engineering knowledge representation ✔ ✔ ✔ ✔
N2 Engineering data integration ✔ ✔ ✔ ✔
N3 Engineering knowledge access and analytics ✔ ✔ ✔ ✔
N4 Efficient access to semi-structured data in the organization and on the Web
✔ ✔
✔
N5 Support for multi-disciplinary engineering process knowledge
✔ ✔ ✔ ✔
N6 Provisioning of integrated engineering knowledge at production-system run-time
✔ ✔
Content
What is the Fourth Industrial Revolution?– Industry 4.0 = flexibility through cyber-physical systems– Manufacturing an important area– Need for flexible production systems and processes (CPPS)– Several scenarios and needs for semantic technologies in the
complex life-cycle of production systems
To what extent can Semantic Web technologies be used to support the scenario of multi-disciplinary engineering?
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CPPS Engineering - Complex scenario
Data complexity Engineering data From different disciplines Complex dependencies Changing Large (40+K signals)
Distributed and concurrent engineering
Different disciplines Different terminology Different tools Heterogeneous data
models and formats
Software Eng.Mechanical Eng. Electrical Eng.
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CPPS Engineering – Example Tasks & QueriesData analysis across data from engineering disciplines
– Comprehensive statistics (across disciplines)– Constraint Checking – Defect detection
Change propagation and notification across disciplines
Software Eng.Mechanical Eng. Electrical Eng.
Which machine functions are needed to produce Product X with Production Process Y?
Which sensors are not linked to a software variable?
Which component contains more than one signal?
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Common Concepts provide a common vocabulary to speak about the data in common They link distributed and heterogeneous (local) data models.
Common Concepts
Data Integration Solution
Ontology Based Information Integration (OBII) Approach Integration of data from
heterogeneous sources using [Cal01, Wac01].
Three components of the OBII approach: – (1) Local ontologies - to
represent data specific to a data source.
– (2) A common ontology - to represent the aggregation of relevant concepts
– (3) The mapping between local ontologies and the common ontology.
Ontology Classification Schemes
Product Production Process
Production Resource
Physical Objects
Ont. of product types, OntoCAPE, eClassOWL
OntoCAPE, ISO 15926
Ont. of resource types, OntoCAPE, CCO, AMLO, ManufOnto, eClassOWL, ISO 15926, AutomOnto
Structure Ont. of product structure, OntoCAPE NF
Ont. of resource structures, OntoCAPE, ManufOnto, EquipOnt, CCO, AMLO,
ISO 15926, AutomOnto
Functionality NFOnt. of production
process types, OntoCAPE, ISO 15926, ManufOnto
Ont. of production resource capabilities (skills), AMLO, ManufOnto, EquipOnt,
ISO 15926
Process NFOnt. of production process structures,
OntoCAPE, ISO 15926, ManufOntoManufOnto
Materials Ont. of bills of materials, eClassOWL NF NF
Observations, Measurements NF
Ont. of process states and its observability, SSN,
OntoCAPE, ISO 15926Ont. of resource states, SSN, AutomOnto
Quantities, dimensions, units
Ont. of product characteristics,
eClassOWL
Ont. of production processes characteristics,
OntoCAPE, ISO 15926
Ont. of production resource characteristics, ManufOnto, CCO, SSN,
AutomOnto
Typical Ontology Modelling Needs
Modelling Part-Whole relations – containment hierarchies are a well-accepted and frequently
occurring organizational paradigm from modelling part-whole relations in mechatronic engineering settings
– No built-in support in OWL but several ODPs Modelling connections between components
– interface-based composition describes the capabilities expected from an interface and can enable reasoning tasks about the correctness of a system’s structure.
Modelling component roles – component roles refer to their functions and behaviour that they play
in the system
Evaluation of Mapping Alternatives
Source: Kovalenko O, Euzenat J (2016) Semantic Matching of Engineering Data Structures. In Biffl S, Sabou M (Eds.) Semantic Web for Intelligent Engineering Applications. Springer
30
Constraint Checking across Engineering Disciplines
“All safe software variables should be linked to exactly two sensors” “Check that all sensors have PLC variables defined”
SELECT ?sensor ?sensor_id WHERE {
?sensor a hw:Sensor . ?sensor hw:hasKeyValue ?sensor_id .
?hw_var a hw:Variable . ?hw_var hw:isDefinedOnDevice ?sensor . ?hw_var hw:hasItemName ?hw_var_name .
OPTIONAL { ?var a cs:GlobalVariable . ?var cs:hasName ?cs_var_name .
FILTER (?hc_var_name = ?cs_var_name) . }
FILTER (!bound(?cs_var)) }SELECT ?kks ?signal WHERE {
{SELECT ?kks WHERE { ?kks :hasSignal ?signal }
GROUP BY ?kks HAVING (COUNT (?signal) >= 2)} ?kks :hasSignal ?signal}}
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Signal List -Version 1 Version 2
Knowledge Change Management – “Change Compression”
25 deletes, 30 updates, 15 insertions.From syntactic level
Pump XA_20 was moved to sector AH1 To semantic level
In real-life scenarios scale is a major issue, e.g:• 40,000 signals• 2,500 deletes, 3,000 updates, 1,500 insertions.
Browsing and Querying of Cross-disciplinary Engineering Data
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AutomationML Analyzer: relies on Linked Data technologies to enable efficient integration, browsing, querying, and analysis of diverse engineering models represented in AutomationML.
Querying Integrated AutomationML Data
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Predefined SPARQL queries enable monitoring, analysis, validation and defect detection tasks
Semantic Web Capabilities for Engineering Settings
Semantic Web Capabilities & Needs N1 N2 N3 N4 N5 N6
C1 Formal semantic modeling ++ + ++ + + +
C2 Intelligent, web-scale knowledge integration
+ ++ ++ ++ ++
C3 Browsing and exploration of distributed data set
+ ++ + +
C4 Quality assurance of knowledge with reasoning
++ ++
C5 Knowledge reuse + + ++
++ +
Summary
What is the Fourth Industrial Revolution?– Industry 4.0 = flexibility through cyber-physical systems– Manufacturing an important area– Need for flexible production systems and processes (CPPS)– Several scenarios and needs for semantic technologies in the complex life-
cycle of production systems To what extent can Semantic Web technologies be used to support the
scenario of multi-disciplinary engineering?– CPPS engineering is a complex scenario– Data integration is crucial– There is a good match between Semantic Web technology capabilities and
the needs of Industrie 4.0 scenarios What are challenges of applying SW technologies?
Challenges
Lack of knowledge acquisition interfaces that are easy to use by engineers– Trend: use SysML and SysML4Mechatronics as front-end for
acquiring ontologies of engineering models Lack of support for mathematical calculations:
– Trend: Hybrid solutions integrating data mining, statistical analysis and relational constraint solvers
OWA not a natural fit for engineering– Must adopt a CWA style presentation of results at the interface level
Dealing with dynamic engineering data– Trend: applying ongoing research in semantic stream reasoning
Outlook
Ample opportunities for using SW in Industrie 4.0 settings– Only one of the four identified scenarios has been well explored– How about other scenarios?
Engineering Phase
Business Requirements
Cyber Physical Production System (CPPS)
Integrate Business Requirements in Engineering
Deploy created artifacts
Production TransportSales
Process Eng.
Electrical Eng.
CAD, Pipe & Instrumentation
Electrical Plan
Tool Data
Tool Data
Customer Representative
Software Eng.
Customer Reqs. & Review
Tool Data
Software Dev. EnvironmentTool Data
Control Eng.
PLC programTool Data
Project Manager
Engineering Cockpit
PLC
Test/Operation Phase
Operator
SCADATool Data
Multi-Model DashboardTool Data
Diagnosis Analysis
Tool Data
OPC UA ServerConfig
ERP SystemTool Data
Production Planning
Tool Data
Business Manager
Production Manager
Control Eng.
PLC programTool Data
Cyber Physical Production System (CPPS)
Access runtime information
Access engineering information
Production TransportSales
Engineering Cockpit
OPC UA Server (augmented)
Business Manager
Enrich runtime information
1
2
3
4
Scenario 1:Engineering
Tool Network
Scenario 2:Multi-disciplinary
Reuse
Scenario 3:Flexible
Production
Scenario 4:Maintenance
Support
Outlook
Ensuring successful SWT uptake by practitioners– Use engineering specific languages as front-ends for the creation of
engineering ontologies (e.g., UML, SysML)– New ontology classification schemes that bridge the needs of
practitioners and SW experts– Better understanding of typical modeling needs and providing
guidelines for solving those, e.g. through Ontology Design Patterns– SW tool evaluation and selection frameworks (e.g., XSL2RDF tools)
Outlook
Extensions to current SW technologies:– High-performance tools that can deal with large, diverse and rapidly
changing datasets Knowledge change management on integrated data sources Managing dynamic engineering data (e.g., stream reasoning)
– Data integration Automatic identification of semantic overlaps between
engineering models More expressive languages to declare mappings between
engineering models– Evaluation of software architectures taking into account the needs of
Industrie4.0 specific applications Further investigating the use of Linked Data technologies in
engineering scenarios