22.4.2005 PROAGE

PROAGE – PROSESSIAUTOMAATION AGENTTIPOHJAISET INFORMAATIOPALVELUT Agent-Based Information Services for Process Automation

Semantic Web Research seminar22.4.2005 / Metso AutomationTampere

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Agenda

Motivation and background Agent Automation

Controlling agents MUKAUTUVA

Information agents PROAGE

Agent services for process automation Conclusions and discussion

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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

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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

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Agent Automation: A Concept of an Agent-Augmented Process Automation System

A g e n t-ba s e d a u to m a t io n la y e r

In s tru m en ta tio n

E x te rna l a g e n t-b a s e d s y s te m s

P roc e s s a u to m a t io ns y s te m

F IPA -b a s e dc o m m u n ic a tion

F IPA -b a s e dc o m m u n ic a tion

E v e n t-b a s e dc o m m u n ic a tion

R e a l- tim ec o n tro l

Agent-Augmented Real-time problem Supervisory control

Subprocess idea Agents are

responsible of certain physical area of the process

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Agent Automation: Control tests with Laboratory test environment

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T2 measurementT2 setpoint

T1 measurement

T1 setpoint

water flow setpoint

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

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MUKAUTUVA: Automaatiosovellustenmukautumisperiaatteet ja –mekanismit

Research 1.4.2003 – 31.12.2004 Controlling Agents and Information

Agents Demonstration scenarios: a real

industrial process

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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

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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

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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

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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

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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

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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

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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

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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

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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??

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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

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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

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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

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PROAGE : Motivation

User interfaces provide the operator with a lot of process data but no refined 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.

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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.

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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

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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

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PROAGESemantic Automation

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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

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discussion / open questions / …

What are the actual needs in process monitoring? Which of these can benefit from agent-

based approach?