Semantically Enriched Industry Data & Information Modelling: A feasibility study on Shop-floor Incident Recognition
Dr. Τhanasis Vafeiadis Information Technologies Institute Center for Research and Technology Hellas [email protected]
14th International Conference on Industrial Informatics Poitiers - 19/07/2016 Outline Introduction CIDEM Semantic Enrichment Feasibility Study Conclusion
14th International Conference on Industrial Informatics Poitiers - 19/07/2016 Introduction
Issues Modelling of industry knowledge is a cumbersome task due to: o Large amount of data; o Lack of automated information-modelling tools; o Continuous emersion of innovative and complex industrial standards; o Fast and continuously changing industrial environment; and o Various heterogeneous sources.
Solution Proposed Shop-floor implicit and explicit knowledge management o Robust integration of multiple existing industrial standards into an open source common information model; o Semantic enrichment of provided information offering machine understandable data to industrial systems; and o Facilitated information flows necessary for the recognition of accidents and path optimization, during working time within high risk shop-floor areas.
14th International Conference on Industrial Informatics Poitiers - 19/07/2016 Industry Knowledge Modelling
Building Information Modelling (BIM) o Still under active and intensive research o Potential for reducing project cost reducing delivery time increasing productivity increasing quality o Average Return-on-Investment (ROI) more than 600% in studied use cases.
14th International Conference on Industrial Informatics Poitiers - 19/07/2016 Industry Knowledge Modelling
What’s the issue? Design and implementation of such models are time consuming, Legal pitfalls Limited range of applications Data Interoperability issues Highly diverse parties Targeted specialization of industrial standards to specific sectors or equipment
The real world, in terms of resources, ideas, events, etc., has to be symbolically defined within physical data stores, thus capturing more meaning of the data through semantic models.
14th International Conference on Industrial Informatics Poitiers - 19/07/2016 Common Information Data Exchange Model (CIDEM)
Translation of the information to a common understandable format
Model information elements o Concepts o Relations o Interfaces
Shared and common vocabulary enabling to address the information needs of various and heterogeneous environments
14th International Conference on Industrial Informatics Poitiers - 19/07/2016 Common Information Data Exchange Model (CIDEM)
Physical storage of the information
Handling (storing/retrieving) heterogeneous information via Restful services
Interoperability with Commercial/Industrial standards (e.g. B2MML, gbXML, etc.)
14th International Conference on Industrial Informatics Poitiers - 19/07/2016 CIDEM Data Structure
XML Schemas o Broad acceptance facilitate information exchange
o Semantics compatible straightforward transition to various ontology formats (RDF/XML)
14th International Conference on Industrial Informatics Poitiers - 19/07/2016 CIDEM Data Structure
Static Information
Dynamic Information
14th International Conference on Industrial Informatics Poitiers - 19/07/2016 Interoperability
Commercial & Industrial Standards o Import complete or partial XML schemas
14th International Conference on Industrial Informatics Poitiers - 19/07/2016 CIDEM API The client communicates with MongoDB database via RESTful web service
HTTP requests call the web-service server
Methods in order to o insert o retrieve o update o delete data in/from the CIDEM Repository
14th International Conference on Industrial Informatics Poitiers - 19/07/2016 Repository - MongoDB An open-source NoSQL database The information is stored in JSON-like documents Related information is stored together for fast query access through the MongoDB query language. Allows to build applications faster, handle highly diverse data types, and manage applications more efficiently at scale Significantly reduced development and operational complexity Ensures data quality with document validation
14th International Conference on Industrial Informatics Poitiers - 19/07/2016 Semantic Enrichment Ontology Design
NeOn Methodology
“A scenario-based methodology that supports a knowledge reuse approach, as well as collaborative aspects of ontology development and dynamic evolution of ontology networks in distributed environments”
Key Aspects A set of 9 scenarios for the construction of ontologies and ontology networks The NeOn Glossary of Processes and Activities Methodological guidelines for a variety of processes and activities of the ontology network development process
14th International Conference on Industrial Informatics Poitiers - 19/07/2016 Semantic Enrichment Ontology Design
Scenario 1: From specification to implementation; Scenario 2: Reusing and re-engineering non-ontological resources (NORs); Scenario 3: Reusing ontological resources.
14th International Conference on Industrial Informatics Poitiers - 19/07/2016 Semantic Enrichment Challenges
Ontologies from pre-defined schemas ( CIDEM ) XML covers the syntactic level, but lacks support for reasoning. Transformation of XML into RDF – RDFised data
14th International Conference on Industrial Informatics Poitiers - 19/07/2016 Semantic Enrichment Approach
XSD-based Ontologies that completely adhere with the schemas Upper Ontology – Hierarchical Structure o Reuse Concepts & Relations o Introduce Domain Concepts
14th International Conference on Industrial Informatics Poitiers - 19/07/2016 Open Semantic Framework (OSF)
An Open Source integrated software stack that through the use of semantic technologies provides: o Data integration across all content and data types o Knowledge management o Semantic search across the enterprise o Distributed, differential data access and permissions, and o Publishing and managing your information. Accessible via the Web that o Available under the Apache 2 license
14th International Conference on Industrial Informatics Poitiers - 19/07/2016 Feasibility Study – Incident Recognition
Safety of the workers in a plant with chemical processes
Facilitate information flows for: o Recognition of accidents o Path optimization for workers’ movement
Towards o Reducing the number of accidents o Giving optimal paths to workers o Providing meaningful information regarding shop floor safety
14th International Conference on Industrial Informatics Poitiers - 19/07/2016 Upper Ontology Shop Floor Ontology Model
Partial Model Shop Floor Workers
Concept Description Worker Operators & Technicians Pilot Plant Small Dedicated Industrial System Area Accessible or Forbidden Area
14th International Conference on Industrial Informatics Poitiers - 19/07/2016 Data Exchange Flow
Thermal Camera Depth Camera Manager Manager
CIDEM
RESTful Web Service
Forbidden Areas
RDFizing & Semantic SPARQL Workers Triple Store Loading Triple Store Manager Engine Processs
Incident Statistics
14th International Conference on Industrial Informatics Poitiers - 19/07/2016 SPARQL Engine
RDF Query Language Retrieving Semantic Results
14th International Conference on Industrial Informatics Poitiers - 19/07/2016 Conclusions
Semantics Enriched CIDEM: o Offers a shared vocabulary for shop floor knowledge; o Ensures easy access and interoperability; o Introduces a multi-layered, accessible and comprehensive information ecosystem to all involved stakeholders;
Through the feasibility study (preliminary results) o Enhanced worker safety o Reduced accidents
14th International Conference on Industrial Informatics Poitiers - 19/07/2016 The End…
Thank you very much for your attention
Questions / Comments ?
This work was partially supported by the EU funded H2020 – IA – 636302 – SatisFactory project
14th International Conference on Industrial Informatics Poitiers - 19/07/2016