CDL-Flex Empirical Research 28.04.2011 Towards Inconsistency Detection during the Design Phase of Automaton Systems Engineering Projects Olga Kovalenko Christian Doppler Laboratory CDL-Flex, http://cdl.ifs.tuwien.ac.at Vienna University of Technology kovalenko@ifs.tuwien.ac.at 1

Current Automation Systems Engineering Practice and its Challenges different terminology, workflow and background of participants non-sequential engineering processes design data represented differently in various tools (technical, syntactic and semantic mismatches) relations and dependencies between different design artifacts are not explicitly captured

Potential risks

Research Objectives & Research Questions CDL Opening, April 12, 2010 Research Objectives & Research Questions Research Objectives RO: Consistency checking and inconsistency detection of design artifacts across the ASE project. Research Questions RQ1: How to explicitly specify the interrelations between the content of heterogeneous data sources across the project? RQ2: How to analyze heterogeneous project data and information regarding the data interconnections in order to perform consistency checking and inconsistencies detection?

Approach: Engineering Knowledge Base [1] CDL Opening, April 12, 2010 Approach: Engineering Knowledge Base [1] 3-layered semantic model: tool ontologies domain ontologies project ontology

Planned Contributions CDL Opening, April 12, 2010 Planned Contributions C1: Represent the design knowledge and design artifacts data in EKB, determine and explicitly specify the interrelations between content of different data sources through mappings. C2: Provide a technique for queries definition and modifying. C3: Identify a set of checks that will be useful towards consistency checking and inconsistencies detection in current ASE practice.

CDL Opening, April 12, 2010 Validation We plan to validate our approach by using a case-study-based approach. Step 1: apply on educational prototype of industrial process plant1. Objective: validate the feasibility of proposed approach. Step 2: apply for real life data from industrial partner – hydro power system integrator. Objective: validate the applicability and relevance of proposed approach for real world industrial needs. 1 “Tank model” of the Odo Struger Laboratory of the Automation and Control Institute at the Vienna University of Technology.

Use Case 1: Tank Model Tank model: Use case: consists of several tanks, valves, and heaters which are connected by pumps. Different types of sensors are used for monitoring of model parameters values. Typical industrial production processes such as heating, settling and mixing of liquids can be simulated by this model. Use case: 3 design models (Piping & Instrumentation Diagram, Electrical Plan, Logic Diagram). 3 ontologies based on initial models with corresponding mappings. “End-to-end” check - whether all hardware devices are properly connected to some PLC variable within the models?

Use Case 1: Ontologies Engineering CDL Opening, April 12, 2010 Use Case 1: Ontologies Engineering Piping & Instrumentation Diagram Logic Diagram Electrical Plan 9

Use Case 1: “End-to-end” Test CDL Opening, April 12, 2010 Use Case 1: “End-to-end” Test We defined mappings between the 3 model ontologies linking each actuator and sensor (P&ID) with unique physical address (Electrical Plan), which, in turn, is associated with specific KKS name. Using the information in ontologies and corresponding mappings we can execute a so called “end-to-end” test, checking whether all actuators and sensors are linked to some PLC variable (which is represented by KKS name). 10

Use Case 2: Hydro Power System Integrator CDL Opening, April 12, 2010 Use Case 2: Hydro Power System Integrator Background: Signals are used as common concepts that link information across different engineering disciplines. Signals include process interfaces (e.g., wiring and piping), electrical signals (e.g., voltage levels), and software I/O variables [2]. Main target is to integrate signals from different tools and to ensure their consistency across the ASE project. Use case: Input data - outcome signals from two engineering tools that are used in the design process. Ontology for each tool with corresponding mappings, which explicitly capture the interrelations between tools data models. Several types of consistency checks: a) within the one signal; b) within a particular set of signals; c) integration test.

Use Case 2: Tools Data Structure Tool 1 data structure Fields “L0” and “Function text” correspond to the same common data model field (kks0) and, therefore, values in these fields must keep this interrelation to be conformed. Tool 2 data structure

Current Status & Future Work Currently we are working on an implementation of a prototypical solution of our approach in a case study based on real world-data from our industry partner, a power plant systems integrator. Definition of checks which will be useful to improve our industrial partner engineering process. Future work will include: Further development of use case based on industrial design data in order to ensure the applicability of proposed approach for a large-scale real-world industrial data. Identification of more sophisticated checks across discipline boundaries that can be applied in a range of ASE projects. Investigation of domain-specific standards with a view to partially derive the requirements for the specification of interrelations between various design artifacts.

CDL Opening, April 12, 2010 References T. Moser and S. Biffl, “Semantic tool interoperability for engineering manufacturing systems,” in Emerging Technologies and Factory Automation (ETFA), 2010 IEEE Conference on, sept. 2010, pp. 1–8. D. Winkler, T. Moser, R. Mordinyi, W. D. Sunindyo, and S. Biffl, “Engineering object change management process observation in distributed automation systems projects,” in Proceedings of 18th European System and Software Process Improvement and Innovation (EuroSPI 2011), 2011, pp. 1–12. A. Wiesner, J. Morbach, W. Marquardt, “Information integration in chemical process engineering based on semantic technologies,” Computers and Chemical Engineering, vol. 35, no. 4, pp. 692–708, 2011 J. Morbach and W. Marquardt, “Ontology-based integration and management of distributed design data,” in Collaborative and Distributed Chemical Engineering. From Understanding to Substantial Design Process Support, ser. Lecture Notes in Computer Science, M. Nagl and W. Marquardt, Eds. Springer Berlin / Heidelberg, vol. 4970, pp. 647–655. H. Wache, T. Vogele, U. Visser, H. Stuckenschmidt, G. Schuster, H. Neumann, and S. Hubner, “Ontology-based integration of information - a survey of existing approaches,” in Proceedings of IJCAI-01 Workshop: Ontologies and Information Sharing, 2001, pp. 108–117.