Semantic Web Research seminar 22.4.2005 / Metso Automation Tampere

1. PROAGE – PROSESSIAUTOMAATION AGENTTIPOHJAISET INFORMAATIOPALVELUTAgent-Based Information Services for Process Automation Semantic Web Research seminar 22.4.2005 / Metso Automation Tampere PROAGE

2. Agenda • Motivation and background • Agent Automation • Controlling agents • MUKAUTUVA • Information agents • PROAGE • Agent services for process automation • Conclusions and discussion PROAGE

3. Process Automation domain • More and more measured and gathered information is stored to different databases • More intelligent field devices available • Distribution of control to subprocess level • Partial diagnostics solutions available • More information available in electronic form; design documents and others PROAGE

4. Agent Automation:Agenttipohjainen automaatioratkaisu • Research 1.6.2000 – 31.3.2003 • Agents and Automation? • Process Automation especially • Fault tolerant control • Abnormal situation handling • Potential process automation functions? • Used technology • Generic agent technology • FIPA standard; negotiations • Robot society research, physical agents PROAGE

5. Agent Automation: A Concept of an Agent-Augmented Process Automation System • Agent-Augmented • Real-time problem • Supervisory control • Subprocess idea • Agents are responsible of certain physical area of the process PROAGE

6. Agent Automation: Control tests with Laboratory test environment • Real demo process • Temperature control & circulation of water • Volume 700 L • OPC connection to PAS • Qualitative process models used • Initial ideas for information access • Idea: more than OPC • “What is the current state of the device?” • “When was the device’s last maintenance check?” • User agent PROAGE

7. MUKAUTUVA: Automaatiosovellustenmukautumisperiaatteet ja –mekanismit • Research 1.4.2003 – 31.12.2004 • Controlling Agents and Information Agents • Demonstration scenarios: a real industrial process PROAGE

8. MUKAUTUVA: Information agents • Challenges • Combining different information from different sources • Adapting to changes in information, environment, physical setup • Information agents used to address similar problems in other application domains • The add-on approach from AGENT AUTOMATION project • new features can be tested on top of current PAS PROAGE

9. MUKAUTUVA:Architecture and the role of agents • Society of hierarchically aligned agents operating in different roles • Client Agent (CA) • User interaction • Information agent (IA) • Information access and procesessing. Also active monitoring • Process agent (PA) • Specialist for some process area, functional or spatial • Wrapper agent (WA) • Provides access to legacy information sources • Directory Facilitator (DF) • Yellow pages - services PROAGE

10. MUKAUTUVA: Demo I – Combining info 12/2003 • Problem: • Combining measurement information from systems with different data format, semantics and query language • Implementation: • Wrapper agents and a common data format • Directory service (DF) • Distributed query • Results: • Basic agent communication & planning defined PROAGE

11. MUKAUTUVA: Demo II – Monitoring 6/2004 • Problem: • Active monitoring of sensors that are vulnerable to defects • Comparing manual laboratory measurements to online data • Implementation: • Task distribution • Data polling & processing at the low level • Subscription protocol • Offline, with actual process data • Results: • Generic monitoring functions, easily configured to a specific task • Problems in combining different languages: FIPA-SL/OWL/RDQL PROAGE

12. MUKAUTUVA: Demo III – State classification 12/2004 • Problem: • Classification of the operational state of a process • Implementation: • Distributed classification • Several active agents • Lower level: state based on process measurements • Higher level: state based on lower level states • Results: • Detection of an actual state transition from actual process data • In general: all but clear PROAGE

13. MUKAUTUVA:Internal design of agents • Internally: separate modules for different information processing tasks • Control: a planning manager module • Action: specialized (e.g. math) information processing modules • Plans: a high-level information processing goal is divided to atomic tasks PROAGE

14. Ontology-based information processing • Motivation • Combining different information from different sources • A computer-processable world / process model • Implementation: OWL • Our ontology is limited & exemplar • Measurements, devices, states… • Concepts derived from standards of the domain PROAGE

15. Conclusions: control • Real-time control not yet feasible • Possibilities in supervisory control • Ecxeption handling • Sequential control • For low-level control: PID etc... • Complex problems require agent-based methods PROAGE

16. Conclusions: information agents • No real-time requirements • Refined information processing on top of current systems: an easy & safe application domain? • MUKAUTUVA: Overall architecture seems OK • Internal design needs a little work • Goal-oriented operation seems reasonable • Math/logic processing?? PROAGE

17. Conclusions: ontologies • There will be no ”universal automation ontology” • A combination of ontologies from different viewpoints • Current domain references are few & from narrow viewpoints • Concept modelling state-of-the-art: XML Schema • Our focus: investigating the mechanisms of ontology-based information processing PROAGE

18. From MUKAUTUVA to PROAGE • MUKAUTUVA demos: general, more or less basic functionalities • State classification still somewhat unclear • How about the process automation services? • Detection of slowly developing faults • State-based alarm filtering • Validation of measurement data • Proactive condition monitoring of devices PROAGE

19. PROAGE • Agent-Based Information Services for Process Automation • 1.1.2005-31.12.2006 • HUT Automation Technology lab • HUT Information Technology in Automation lab • VTT Industrial Systems • Metso Automation, UPM, Teleca PROAGE

20. PROAGE : Motivation • User interfaces provide the operator with a lot of process data but norefined knowledge about the process state and performance. • Combining different existing monitoring and diagnostics solutions is difficult. • Process models or simulators cannot be efficiently utilized in condition monitoring, if the state of the process is not known. PROAGE

21. PROAGE : The Goal • Project goal is to design intelligent and cooperative condition monitoring and maintenance services for e.g. process operators. • These services are based on information agent and Semantic Web technologies. • Idea: Information agents operate as a team that extends the state awareness of human users. PROAGE

22. PROAGE : Work Packages • Defining the information agent services • Services relevant to industry • Services suitable to agent-based approach • Further developing the information agent system architecture • Demonstrating the agent services • Goal: online, on-site • Outlining a roadmap for adoption of agent-based solutions in automation PROAGE

23. PROAGE : Potential ideas for services • State-aware, model-based condition monitoring • Adaption of fault diagnostics to exceptional operational states • ”this is not a problem, since we are shutting down” • Metadata labelling of measurement history • More abstract, refined information to user interfaces • From direct variables to calculated variables PROAGE

24. PROAGESemantic Automation PROAGE

25. discussion / open questions / … • Our focus: investigating the mechanisms of ontology-based information processing • Creating suitable ontologies for the domain a substantial challenge • ”Someone else will do it” • Our challenges • No W3C specification yet for: • A query language • Logic & math on top of OWL • How to combine goal-based planning with ontology-based information processing PROAGE

26. discussion / open questions / … • What are the actual needs in process monitoring? • Which of these can benefit from agent-based approach? PROAGE