Semantic Web Enabled Network of Maintenance Services for Smart Devices

Agora Center, University of Jyväskylä, March 2003

“Industrial Ontologies” Group Tekes Project Proposal

http://www.cs.jyu.fi/ai/Metso_Maintenance.ppt

Our Team: “Industrial Ontologies” Group

Head: Vagan Terziyan

Researchers: Oleksandr Kononenko Andriy Zharko Oleksiy Khriyenko

Supervisor and Consultant from Metso:

Jouni Pyotsia

vagan@it.jyu.fi Agora Center, University of Jyväskylä

“Industrial Ontologies” Group: http://www.cs.jyu.fi/ai/OntoGroup/index.html

Emerging Semantic Web “Knowledge is an important productivity factor” However to make your knowledge to be really such you

should consider managing it based on emerging Semantic Web Technology

Then it would be possible to take better care of your businesses, products, services, processes, etc. using automatically collected and integrated experience from different heterogeneous distributed sources worldwide

This makes possible also to make your own knowledge and experience reusable, shared and permanently beneficial

Enterprise Integration Technologies

Web Service Technology (SOAP, WSDL and UDDI); Enterprise Integration (Enterprise Application Integration and

E-Commerce in form of Business-to-Business Integration as well as Business-to-Consumer);

Semantic Web Technology (ontology languages).

The promise is that Web Service Technology in conjunction with Semantic Web Technology (“Semantic Web Services”) will make Enterprise Integration dynamically possible for all types and sizes of enterprises compared to the “traditional” technologies

Semantic Web

http://www.w3c.org/2001/SW

“The Semantic Web is a vision: the idea of having data on the Web defined and linked in a way that it can be used by machines not just for display purposes, but for automation, integration and reuse of data across various applications”.

Ontological Vision of Semantic Web

Semantic Web needs ontologies

An ontology is document or file that formally and in a

standardized way defines the hierarchy of classes within the domain, semantic relations among terms and inference rules

Use of ontologies: Sharing semantics of your data across

distributed applications

Knowledge Management based on Semantic Web concepts

A commitment to a common ontology is a guarantee of aconsistency and thus possibility of data (and knowledge) sharing

It seems feasible to use standards of the Semantic Web research community for the development of next-generation information systems based on ontology-driven knowledge management, e.g.:

Intelligent process automation systems Intelligent condition monitoring systems Decision support systems (embedded AI) Intelligent maintenance systems and services …

Project Primer Goal

The primer goal is to study and implement the benefits of the:

Semantic Web (interoperability based on ontological support and semantic annotations),

Intelligent Web Services (modelling, automated discovery and integration), and

(Multi)Agent technologies (agents communication, coordination and mobility)

… to improve the performance of the Field Device Management Process by launching a network of distributed intelligent maintenance services.

Pilot Implementation Goal .

More specifically the goal is to develop: a prototype of a global intelligent diagnostics

and maintenance support system, an appropriate multiagent support for it, ontological support for it, pilot prototype implementation, case study.

New vision assumes a Maintenance Services Network of smart-devices and Maintenance Service Centers, in which maintenance experience is accumulated independently by agents of each Maintenance Center with a possibility to be integrated together when needed. Smart-devices are becoming users of provided maintenance services.

Global vision: agents in action

Agents acting as service components in the Maintenance Service Network have ability to learn during work improving services’ performance.

Challenge 1: Service Users are devices

The class of service requestors is extended with new group of service users – smart devices.

We add semantic-enabled descriptions of services to facilitate:

automated discovery and use of services by smart-devices;

automated integration of services; communication between heterogeneous services.

I’m competent in domain 1..

I’m competent in domain 2..

I’m competent in domain N..

I have a problem from domain 23..

Who can help?

As a result of independent maintenance experience accumulation by service components (agents) every Maintenance Service Center in the net provides specific set of service components. When a problem arises maintenance service components with the most relevant knowledge for that case might be found in the net.

Distributed knowledge

Internal and External Agent Platforms

Service PlatformEnvironment where service components perform:• Condition monitoring• Maintenance activities

Based on the online diagnostics, a service component-agent, selected for the specific faulty or emergency situation, can be moved to the service platform to help the host agent to manage it and to carry out the predictive maintenance activities.

Maintenance PlatformEnvironment to run Maintenance Services, contains a set of expert-agents both in maintenance and diagnostics. Agents are “service components”

Challenge 2: Two Types of Service Platforms

Service Platform is an environment for running services and hosting service components (agents).

Services can be provided either locally, i.e. by embedding them to smart-device internal platform, or remotely by querying them from a Web-based external platform.

External service can be queried either from Web-based external platform or from another internal platform.

External Web service platforms provide more rich services since they are used by many clients and quality of services can be permanently improved according to growing experience.

Various interactions between service platforms (internal-internal, internal-external, external-external) can be organized as a P2P-like network.

Internal Platform

Field Agent – device-dependent embedded condition monitoring component (e.g. FieldBrowser);Wrapper component – for integration with device-dependent (software and hardware) resources, acts

as a semantic adaptor, mediator between semantic-enabled and traditional parts of service infrastructure;

Management components – for management of maintenance activities and distributed resource allocation;

Diagnostic components – for online discovery of problems within a device based on its state parameters and ontology-based classification of these problems (component is mobile agent);

Recovery components – for automatic planning and performing appropriate maintenance activities for a discovered diagnosis (component is mobile agent).

Diagnostic components

Recovery components

Management component Wrapper component

Field Agent

External Platform

Management component, Diagnostic components, Recovery components – service components of Maintenance Service Center.

There is similar service components set as in the Internal System structure, but these components have more rich “experience” and abilities to solve problems.

Recovery components

Management component

Diagnostic components

Agents in Semantic Web

1. “I feel bad, pressure more than 200,

headache, … Who can advise what to do ? “

4. “Never had such experience. No

idea what to do”

3. “Wait a bit, I will give you some pills”

2. “ I think you should stop drink beer for a while “

Agents in Semantic Web supposed to understand each other because they will share common standard, platform, ontology and language

The Challenge: GGlobal UUnderstanding eNNvironment (GUNGUN)

How to make entities from our physical world to understand each other when necessary ?

GUN Concept

Entities will interoperate through OntoAdapters,

which are “supplements” of these entities up to Semantic

Web enabled agents

1. “I feel bad, temperature 40, pain in stomach, … Who can advise what to do ? “

2. “I have some pills for you”

Semantic Web: Before GUN

Semantic Web Resources

Semantic Web Applications

Semantic Web applications “understand”, (re)use, share, integrate, etc. Semantic Web

resources

GUN Concept:GUN Concept: All GUN resources “understand” each other

Real World objects

OntoAdapters

Real World Objects ++ OntoAdapters =

= GUN ResourcesGUN Resources

GUNGUN

Maintenance Services

Organizing the maintenance …

• Service 1: Remote diagnostic• Service 2: Recovery and predictive

maintenance• Service 3: Preventive inspection• Service 4: Emergency service• Service 5: Human resource execution• …

•Alarm situation is locally detected however Internal Maintenance Platform (IMP) is not able to classify it as certain diagnosis. Thus IMP sends request with parameters to an External Maintenance Platform (EMP).•As a result, EMP sends discovered diagnosis back to the IMP. •If similar request for diagnosis is sent often enough, then it is considered to send appropriate diagnostic service component (mobile agent) from EMP, to operate locally at the IMP.

parameters

diagnosis

Agent with knowledge

parameters

diagnosis

Remote diagnostics scenario

Challenge 3:

Service Components are Autonomous Intelligent Agents

Service components are mobile;Service components are able to learn;Service components are Semantic Web

enabled

Requirements for Management Service component:• Check of request correspondence to available local services, based on profile of MC.• Request to other components of the network, in case if request can’t be satisfied.• Enabling peer-to-peer semantic search in the Maintenance Service Network

Service components are certified. Certification system is a basis for guaranteed quality of maintenance services.

Maintenance Service Network

All interactions in the Maintenance Network are performed between Management Service components

Service management

MC

High-level functions are performed on the base of profile processing. Each Maintenance Service Center has a corresponding profile which describes its services. Profile is created in machine understandable form on a basis of common ontology.

Profile is a file, that contains information about:- what type of maintenance activities MSC provides;- what level of quality it’s gained during certification;- economical aspects (cost).

Since we have independent services in distributed environment, thepeep-to-peer concept must be impliedon base of Semantic Web (profile web).

Challenge 4: Semantic P2P Concept for Service network Management

The concept assumes decentralized management architectures with.centralized ontologies for e.g.:

Service certification management; Service discovery management; Service responsibility management; Quality of Service management; Trust management; Privacy and security management .

Also transaction management issues related to transportation of mobile.components between platforms should be addressed in this project.

Two levels of management are considered: for interactions between.local service platforms of smart-devices (P2P network) and for.interactions between service centres on enterprise level.

Subdomain ontologiesThe following

set of subdomain ontologies can be defined:

describes device structure, its components and states

(for maintenance/control processes)

describes breaks and faults classifications, maintenance cases

bindings to certain products or components, specification of detection

methods, rules, etc.

describes maintenance activityclassification and prerequisites of

use: rules/inference tools to use, etc.

Maintenance Maintenance ActivityActivity

Class of Maintenance

Activities

Subclass-of

DiagnosisApplied-to

Requires

Resource

Class of Diagnosis

Subclass-ofRestricted-by

Class of Resources

Subclass-of

Restriction

Class of Restrictions

Subclass-of

Procedure

Specification

Applied-to

State of product

Standardized-by

StandardClass of StandardsSubclass-of

ProductState

Upper Maintenance Ontology

Creating ontologies…

Classes hierarchyClass properties

(“slots”)

Class details

RDF in XML

RDF: description

RDFS: vocabulary

Ontology of Control Valves with Protégé

DesignMaintenance Centers

Infrastructure

Design Pilot Service

Platforms

Our project implementation goals

Provide minimal set of necessary information

structures and ontologies

Implementminimal set of

maintenance service components (agents)

Necessary data for pilot implementation

To select some product as a case for implementationand consider different diagnostic cases.

How equipment state is described?

What breakage classes exist?

What is the relation ’equipment state’ – ’breakage class’

What maintenance activities exist?

How does ’breakage class’ associate with

’maintenance activity’

What is the relation ’maintenance activity’ –

’equipment state’

Project Main Objectives

Development of upper-ontologies for the maintenance domain Development of samples: (a) an embedded agent-enabled platform and

(b) Semantic Web maintenance service for smart-devices Development of P2P semantic search techniques in semantic-enabled

network of maintenance services Pilot implementation of embedded platform and set of maintenance

services Development of ontology for Metso smart-device case Testing of pilot system on the Metso smart-device case

Project Deliverables1. Requirements to a Maintenance Service Network for Smart-Devices2. Requirements to possible service components (agents)3. Requirements to an embedded service platform4. Requirements to ontology management in a semantic P2P network5. Requirements to maintenance service ontology6. Scenarios for certification, security, privacy and trust management7. Service platform specifications and implementation plan8. Upper-ontologies for smart-devices’ maintenance domain

Devices ontology Diagnostics ontology Maintenance activities ontology Maintenance service ontology

9. Pilot implementation of the Service Platform

Conclusions Traditional Enterprise Integration technologies are able to address

some of maintenance management problems today. However, new technologies like Web Services Technology in combination with Semantic Web and Agent Technologies have the potential to address maintenance needs much better

We have experience and human resources to develop the concept of Distributed Maintenance Network and provide implementation starting from a pilot system and pilot ontologies

Results can be used by co-operating companies: e.g. Metso for management of their field devices based on embedded agent platforms and Web services; Sonera for providing communication infrastructure for embedded agents and launching appropriate Web services for this and also for other cases