Ontology-Assisted Engineering of Cyber–Physical Production Systems in the Field of Process Technology

Future cyber–physical production systems (CPPS) constitute a complex and dynamic network of services and plant components such as actuators and sensors. Consequently, the manual technical specification and design of these systems are a complex and time-consuming task involving extensive expert knowledge. In scope of CPPS, current approaches to reduce the engineering effort focus on manufacturing technology. There are initial approaches in the domain of process engineering available. However, these neither consider the knowledge-supported definition of recipe-based operations nor the assignment of dynamic service networks to process modules. The objective of this contribution is the design of a concept and a systematic approach to automate the engineering of batch process plants respecting dynamic service networks and process modules using a knowledge-based assistance system. For this purpose, a declarative recipe description is combined with an ontological model. This enables an automatic inference of technical requirements. Based on this information, a multistage orchestration algorithm selects and combines process modules and networked services to find appropriate engineering solutions. Finally, a comprehensive case-study demonstrates that the proposed approach is able to automate the target-oriented selection and combination of process modules and service networks.

[1]  Oliver Niggemann,et al.  A descriptive engineering approach for cyber-physical systems , 2014, Proceedings of the 2014 IEEE Emerging Technology and Factory Automation (ETFA).

[2]  François Jammes,et al.  Service-oriented paradigms in industrial automation , 2005, IEEE Transactions on Industrial Informatics.

[3]  Noël Crespi,et al.  Semantic Context-Aware Service Composition for Building Automation System , 2014, IEEE Transactions on Industrial Informatics.

[4]  Mario Vento,et al.  Challenging Complexity of Maximum Common Subgraph Detection Algorithms: A Performance Analysis of Three Algorithms on a Wide Database of Graphs , 2007, J. Graph Algorithms Appl..

[5]  Thomas Greiner,et al.  Application-independent approach for the dynamic management of IT-resources in cyber-physical systems , 2016, 2016 IEEE International Conference on Industrial Technology (ICIT).

[6]  Marcus Grünewald,et al.  Net Present Value Analysis of Modular Chemical Production Plants , 2011 .

[7]  Thomas Greiner,et al.  Semantic subgraph isomorphism for enabling physical adaptability of Cyber-physical production systems , 2016, 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA).

[8]  Edward A. Lee,et al.  Introduction to Embedded Systems - A Cyber-Physical Systems Approach , 2013 .

[9]  Jan Morbach,et al.  OntoCAPE: A Re-Usable Ontology for Chemical Process Engineering , 2009 .

[10]  Johan Springael,et al.  Design of a chemical batch plant with parallel production lines: Plant configuration and cost effectiveness , 2017, Comput. Chem. Eng..

[11]  Georg Schitter,et al.  A service-oriented domain specific language programming approach for batch processes , 2016, 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA).

[12]  Jürgen Beyerer,et al.  Plug & produce by modelling skills and service-oriented orchestration of reconfigurable manufacturing systems , 2015, Autom..

[13]  Stefan Biffl,et al.  Supporting the engineering of cyber-physical production systems with the AutomationML analyzer , 2016, 2016 1st International Workshop on Cyber-Physical Production Systems (CPPS).

[14]  Oliver Niggemann,et al.  Why Cyber-physical Production Systems Need a Descriptive Engineering Approach – A Case Study in Plug & Produce , 2014 .

[15]  Valeriy Vyatkin,et al.  Ontology Driven Approach to Generate Distributed Automation Control From Substation Automation Design , 2017, IEEE Transactions on Industrial Informatics.

[16]  Bilal Ahmad,et al.  Engineering Methods and Tools for Cyber–Physical Automation Systems , 2016, Proceedings of the IEEE.

[17]  Leon Urbas,et al.  Engineering method for the integration of modules into fast evolving production systems in the process industry , 2015, 2015 IEEE International Conference on Automation Science and Engineering (CASE).

[18]  Matthias Loskyll,et al.  Context-Based Orchestration for Control of Resource-Efficient Manufacturing Processes , 2012, Future Internet.

[19]  Ana Paula F. D. Barbosa-Póvoa,et al.  A critical review on the design and retrofit of batch plants , 2007, Comput. Chem. Eng..

[20]  Nicola Guarino,et al.  Semantic Matching: Formal Ontological Distinctions for Information Organization, Extraction, and Integration , 1997, SCIE.

[21]  José L. Martínez Lastra,et al.  Semantic web services in factory automation: fundamental insights and research roadmap , 2006, IEEE Transactions on Industrial Informatics.

[22]  Carmen Constantinescu,et al.  A knowledge-based tool for designing cyber physical production systems , 2017, Comput. Ind..

[23]  Wael M. Mohammed,et al.  Cyber–Physical Systems for Open-Knowledge-Driven Manufacturing Execution Systems , 2016, Proceedings of the IEEE.

[24]  Birgit Vogel-Heuser,et al.  Guest Editorial Industry 4.0-Prerequisites and Visions , 2016, IEEE Trans Autom. Sci. Eng..