the integrated environmental monitoring (iem) project€¦ · classification: restricted iem...

IEM project Classification: Restricted

The Integrated Environmental Monitoring (IEM) project

Approach for development & implementation of a semantic model for the environmental domain


Agenda

• Introduction Kaare Finbak, Kongsberg

• Ontology construction in IEM Morten Strand, DNV

• Semantic Challenges in Integrated Akram Bou-Ghannam & Environmental Monitoring Jone Aarre, IBM Approach to implementation of model

• Summary Kaare Finbak, Kongsberg


Introduction

Kaare Finbak, Kongsberg


The Integrated Environmental Monitoring (IEM)

project

Statoil R&D in Nov 2011 awarded Kongsberg, DNV & IBM a three year contract for joint development of a solution for Integrated Environmental Monitoring (IEM)

New concept

Proactive monitoring of environmental status

Part of day to day operations


The IEM concept


Main components of solution to be developed

Semantic Model for the IEM domain

5. Environmental data analysis

4. Information work spaces,

collaboration and work practices

3. Information access, interfaces,

integrity and security

2. Communications infrastructure

1. Sensor technology


Depiction of development process

Scenarios Use cases

Work processes, methods & prototypes

Demonstra-tions

Life cycle focus

• Open area

• Seismic & drilling

• Installation

• Production

• Decommissioning

and after

Two first use cases

• Monitoring of drill

cutting discharges

• Production leak

detection

Prototypes

• Technologies to be

provided by IBM,

DNV and

Kongsberg

Structured evaluations

• Improvements to work

processes, methods &

prototypes

Dec 2011 Dec 2014


Why a semantic model

• Need to present data from multiple sources in context

• Need to access data across domains, facilities, companies and institutions

• Want to have the opportunity to infer logical facts about instances

• Want to share environmental data with other companies, institutions and the public


Goals of the semantic part of the IEM project

• Develop a semantic model for the environmental area

• Implement it in the solution that is to be developed, i.e., in the integration layer provided by IBM

• Demonstrate its ability to integrate data from various data sources and present meaningful data to applications

• Provide basis for future decisions on how to

• make model and environmental data available for others

• take the model forward to an approved standard


Approaches discussed

Implementation of model

Development of model • Use-case driven ontology development

• Modular development

• Controlled use of external resources

• Apply appropriate Industry and W3C

standards as supported to instantiate

model

• Link to data to access authoritative data

• Map linked to data using model aware

approach

Access to model & data • Recommend how to take the model to an

approved standard

• Recommend how to make environmental

data available to others

To be presented

by DNV

To be presented

by IBM


Development of the IEM ontology

Morten Rørvik Strand

Det Norske Veritas


Motivation

The ontology will form the basis for the IEM integration model, and as such it must be robust enough to

• be the common nomenclature for all IEM activities and

• span multiple disciplines

• handle that many people will be involved in its development.

To do that, the IEM project must :

• Ensure that consistency is preserved.

• Tie-in to already existing and relevant domain libraries

• Be available and accessible

• Use the right open standards (Correct standards, OWL2)


Content

• Use-case driven ontology development

• Tight cooperation between domain experts and ontology experts

• Frequent coordination with data sources and use cases

• Modular development

• Don’t let unrelated domains interfere with each other

• Ensure that common concepts are centrally defined

• Controlled use of external resources

• Be aware of implications of using external resources.


schemas

Identify domains and consolidate information

Ontology Experts

Domain Experts

Nomenclature

knowledge

patterns raw data

Ontology


Identify domains and consolidate information

Ontology Experts

Domain Experts

Ontology

Domain Data


Modular ontologies: Importing

A

B

If a resource R is in A and B, we may say

Specific

ity • R is a subclass of X

• X is a subclass of R

• X is a subclass of R

• R is a subclass of X


Selecting an Upper Ontology

Upper Ontology

Domain ontology A Domain ontology B


The Ontology Stack: Partitioning of the Ontology

Upper Ontology

Core Ontology

Domain ontology A Domain ontology B


External resources: Linking Data

JORD RDL

IEM Ontology Stack

Upper Ontology

Some ontology

Other public data source

NPD Fact Sheets


External resources: Linked Data and reasoning

A

B

C – External

weak strong

weak

Reasoning along strong links

No reasoning along weak links


The Whole Ontology Stack

etc

Upper Ontology

NPD

PCA RDL

Core

Domain 1

Domain 2

Domain 4 Domain 3


Semantic Challenges in Integrated Environmental

Monitoring – Approach to implementation of model

IBM Industry Solutions

Akram Bou-Ghannam, Jone Aarre,

Semantic Days Conference, Sola

10 May 2012


Harness the power of information through Integrated

Environmental Monitoring

SensorsSensors

Platform

RT

Platform

RTPlatform

RT

Platform

RT

ExternalExternal

Statoil

System

Statoil

System

Statoil

System

Statoil

System

Enterprise Service BusEnterprise Service Bus

IEMIEM

RTRTPlatform

RT

PlatformPlatform

RTRT

Statoil

System

s

Statoil

System

s

IEM KBIEM KB GISGIS

External

www

External

www

GODIGODI

Manage Model

Security

QualityAdapters Rule Engine

Access

Service Data Management

RSMRSM

AnalyticsAnalytics•Environmental

expertise

•Risk expertise

•Models

•Tools and

technology

Data Communication InfrastructureData Communication Infrastructure•Wireless network

•Cabled

•Power system

Workspace, collaboration and work practicesWorkspace, collaboration and work practices•Operation with RT monitoring

•Historical view of environmental data

Data Access and managementData Access and management

SensorsSensors

Platform

RT

Platform

RTPlatform

RT

Platform

RT

ExternalExternal

Statoil

System

Statoil

System

Statoil

System

Statoil

System


IEMIEM

RTRTPlatform

RT

PlatformPlatform

RTRT

Statoil

System

s

Statoil

System

s

IEM KBIEM KB GISGIS

External

www

External

www

GODIGODI

Manage Model

Security


Access


RSMRSM


expertise

•Risk expertise

•Models

•Tools and

technology


•Cabled

•Power system




• Operational Business Intelligence

• Standards-based Information Model [Reference Semantic Model]

– Provides much needed context for integration, interoperability, & Analytics

• Predictive Analytics

• Improve Operational Decision Making

• Anticipate operational risk Requirements, and execute in advance of critical points

– Monitor Assess Predict Optimize

Key enablers


Environmental Monitoring Information Requirements

• Condition Monitoring requirements:

– Support operations, condition monitoring, measurements, data collection, diagnostics, early warning detection, equipment definitions, control, processes, interoperability and life-cycle events of a monitored entity

• Integration requirements

– Semantic Consistency

• Provide a semantically consistent view of IEM data across the extended enterprise

• Minimize uncertainty – maximize context

– Flexibility and ease of cross domain integration

• Accelerated integration using Linked Data and Semantic consistence as required by implementation platform

• Reduce costs of accommodating changes

• Flexibility to easily & quickly reorganize data to accommodate new business needs

• Flexibility of perspective in presenting IEM data


IBM’s Information Integration Core (IIC) – A model aware

integration framework for managing Data Access in IEM

SensorsSensors

Platform

RT

Platform

RTPlatform

RT

Platform

RT

ExternalExternal

Statoil

System

Statoil

System

Statoil

System

Statoil

System


IEMIEM

RTRTPlatform

RT

PlatformPlatform

RTRT

Statoil

System

s

Statoil

System

s

IEM KBIEM KB GISGIS

External

www

External

www

GODIGODI

Manage Model

Security


Access


RSMRSM


expertise

•Risk expertise

•Models

•Tools and

technology


•Cabled

•Power system




SensorsSensors

Platform

RT

Platform

RTPlatform

RT

Platform

RT

ExternalExternal

Statoil

System

Statoil

System

Statoil

System

Statoil

System


IEMIEM

RTRTPlatform

RT

PlatformPlatform

RTRT

Statoil

System

s

Statoil

System

s

IEM KBIEM KB GISGIS

External

www

External

www

GODIGODI

Manage Model

Security


Access


RSMRSM


expertise

•Risk expertise

•Models

•Tools and

technology


•Cabled

•Power system




• Start with RSM as the meta model for IEM

– Hypothesis: IEM model requirements are very similar to the requirements for Condition Monitoring and Diagnostics (CM&D) of engineering assets

• Identify needed RSM extensions and link other models when & where needed

• IIC – a model aware integration platform

• Reference Semantic Model (RSM) – a key component

• RSM is built by linking multiple industry standards to form an enterprise wide Ontology

• RSM includes a Condition Monitoring ontology

Approach


Machine Condition Assessment Process is the same as the

Environmental Condition Assessment Process

Monitor

Determine

Condition /

State

Assess

Health

Assess

Future

Health

Provide

Actionable

Information

Sensors Condition Monitoring

Systems Predictive O&M Solutions / Systems

What variability is preventing the asset from delivering the

performance it was meant to have?

DA DM SD HA PA AG

Is the asset operating as designed?

Diagnostics

What is the estimated time to failure based on projected

usage and historical data?

Maintenance

/ Operations

Processes

EAM, ERP

Prognostics

What are the recommended maintenance and

operational changes?

Advisories

Identify, predict, & manage asset operational risk:

1) Asset Health; 2) Environmental Health & Safety;

3) Process Safety; 4) Quality


Extending the RSM to meet the Integrated Environmental

Monitoring requirements – A proposal

S 95 + S 88 S 95 + S 88

ISO 15926 ISO 15926

UN/CEFACT UN/CEFACT

CIM CIM

OPC OPC

MIMOSA MIMOSA

SI Base Units

SI Base Units

Reference Semantic Model

S 95 + S 88 RSM

ISO 15926 ISO 15926

UN/CEFACT ISA 95/88

CIM OPC

OPC UNCEFACT

MIMOSA Open O&M /

MIMOSA

SI Base Units

SI Base Units

1

Semantic Sensor

Network

2 • RSM – built by linking

multiple industry standards to form an enterprise wide ontology

• Condition Monitoring Ontology in RSM created from Open O&M / MIMOSA standards

• Link RSM with Semantic Sensor Network (SSN) ontologies

1

2

Others?


Modeling Environmental Monitoring using RSM – Example

Hyme Field (Site)

H3 Coral Reef Area (Segment)

*

Temp. Transducer (Transducer)

Water Temperature (MeasurementLocation)

Measure

05 (Value)

Celsius (Value Type)

Water Temp (Measurement)

Registers

has

has

Lander (MeasurementSource)

Data: Measure

(Enterprise)

Registers Site

(AttributableEntity)

*

Segment (MonitoredEntity)

Registers

*

Asset (MonitoredEntity)

Registers

*

MeasurementLocation (AttributableEntity)

has

* Measurement

(HierarchicalEntity)

has

Transducer (Asset)

MeasurementSource (Asset)

0..1

0..1

Measurement Attributes:

-- Data: Is the actual measurement value and

could be of any Type specified in the

ValueContent enumeration

-- Start, Stop, & other time stamp attributes

-- Confidence, etc.

class 01 - Core Types

«XSDchoice»

ValueContent

+ BinaryData :BinaryData

+ BinaryObject :BinaryObject

+ Boolean :Boolean

+ CDF :CDF

+ Coordinate :Coordinate

+ HDF5 :HDF5

+ MathML3 :MathML3

+ Measure :Measure

+ MonetaryAmount :MonetaryAmount

+ Number :cct:NumericType

+ OCL :string

+ Percentage :Percentage

+ Probabil ity :Probabil ity

+ SVG :SVG

+ Text :cct:TextType

+ TextWithUnit :TextWithUnit

+ UTCDateTime :UTCDateTime

+ UUID :UUID

+ XHTML :XHTML

+ XMLAny :XMLAny

Enterprise (AttributableEntity)

*

Registers

*

Registers


Summary

• Presented an approach for modeling the Real-time Environmental Monitoring domain

• Strategy: Build upon current (existing) RSM and extend it to satisfy the Environmental Monitoring domain.

• Rationale: Even though the RSM is build on standard ontologies from the Operations & Maintenance (O&M) domain - we feel it is generic and flexible enough to be extended and instantiated for IEM concepts. Specifically, the Condition Monitoring, Measurements, and data collection concepts.

• To Do:

– 1) Look into integrating the Semantic Sensor Network (SSN) standard ontology. Identify what parts of the SSN are needed and how to make the SSN and RSM ontologies consistent.

– 2) Identify needed RSM enhancement [Support for the updated UNCEFACT ontology, and additional needed Open O&M MIMOSA-CCOM concepts].

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