Bringing trust to the Internet of Things · 2017. 3. 13. · Sources Business value Volume (real time) Characteristics New Capabilities Behavioural data Velocity (real time) ... Plan

DNV GL © 2017 20 January 2017 SAFER, SMARTER, GREENERDNV GL © 2017

08 March 2017

Bringing trust to the Internet of ThingsValuable insights from data to support critical decisions in industryJørgen Kadal DNV GL

20 January 2017

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DNV GL © 2017 20 January 2017

Global organisasjon – lokal kunnskap

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250kontorer

100land

14,000ansatte

150år


Fem forretningsområder

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OIL & GASMARITIME ENERGY BUSINESS ASSURANCE

SOFTWARE


Digitalisation will impact even DNV GL

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Digital disruptive forces

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Target for disruptive digital initiatives

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Trapped value Transactional friction


Industry is full of transactional friction and trapped value

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The potential impact from digitally-enabled initiatives is high

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The rise of digitalization

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TODAY

• Devices are generating 277 times as much data as people• 30 million new devices are connected every week• Only 25% of companies report having a strategy for

digital transformation

Source: Cisco


The rise of digitalization

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THE FUTURE

• 78% of all computing will be done in the cloud (2018)• 40% of all businesses will be disrupted by a digital issue

(2019)• Connected systems will each generate 5TB of data per

day (2020)• There will be 50 billion connected devices (2020)

Source: Cisco


Big data/IoT Analytics–some key identifiers and business value

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Sources Business value

Volume(real time)

Characteristics New Capabilities

Behavioural data

Velocity(real time)

Variety(Sensor,

transaction, text etc.)

Veracity(Truthfulness)

Sensor data

Geospatial data

Transaction / Contributory data

Technology

Competence

Data management & Governance

Target marketing

Connectivity and data sharing

Health diagnostics

Logistics and optimisation


Managing risk in 1864

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Analogue trust

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Industry 4.0 - Toward data-driven decision making

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Manufacturer

Operator

Authority

Asset owner

Service provider

Systems integrator


IoT Revolution promise new Data enabled outcomes for Industry

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Demonstrate Compliance with regulations

Manage operation related cost riskOptimise productivity

Drive towards sustainable operation

Asset Owners


Predicitive maintenance promise huge gains across industries

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5 10 15

Con

ditio

n

TimeNormal 5 year maintenance

interval

20 % reduction in cost

Pridicive alogrithm can tell when next problem is likely to occurr given the operating mode. Chaning operating mode may further postopne problems

Plan maintenance according to prediction


But – You do not want to end up here

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Digitalisation of DNV GL –

we need to learn Big Data analytics to continue to deliver safety assurance and leading advisory services to our customers

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Invest in new competencies - DNV GL digital accelerator

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Leading global teams of;• Data scientists• Data engineers• Software developers• Domain experts

• More than 15 nationalities, 4 locations


Invest in new technologies – research on Big Data technologies over the past 6 years.

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Fantorangen:

Serious size

• 21 nodes

• 680 cores

• 2.2 TB RAM

• 1.3 PetaBytes of raw data

Smalldata: big data:

§ 10 nodes

§ 52 cores

§ 240 GB RAM

§ 72 TB raw storage

webofdata.wordpress.com

Cloud:


Invest in new technologies - Azure Cloud Data Management platform capabilities

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Running pilots with customers - Condition monitoring of drillship from sensor data

Case description:DNV GL and partners have executed a Joint Development Project to develop an assurance framework enabling use of big data analytics for condition monitoring and failure prediction of key systems and components from sensor data.

Data facts:• 2 Drillships• 3 Main Systems• 15000 sensors• 36 billion records• Up to 1 year of data

Need updated picture of rig here, not the right picture.


Using sensor information, most bearing, winding, brush and shaft failures are predictable. Periphery failures lead to comparably short downtimes.

ÞPortion of surprising failures w.r.t downtime could become much lower than 50%

Motor winding wear is highly temperature dependent (cable isolation breakdown).

10 degrees temperature increase can double ageing.

Running pilots with customers: Electric motor effective age based on temperature data

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Turbine ID 10

Running pilots with customers - Detection of deteriorating conditions on wind farms: Data flagging automation

Consumer benefits

• Able to get better and more continues evaluation and forecasting of windfarm performance

• Ability to compare performance across own windfarms and turbine manufacturers

• Ability to compare performance across the industry through benchmarks

Case description:

Forecast and assessment of windfarm performance – detection of deteriorating conditions from sensor data (wind speed, power, pitch, wind direction,++).


Digital trust – challenges and opportunities

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Lack of trust in the data value chain

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Wrong Decision

from datae.g.

Component fails while Algorithm says Ok

Sensors and

metadataNetworks

Data Logging

and storage

Data Mgmnt.

Modelling and

Algorithmsxxxxxxxxxx

Mitigation of causes Mitigation of consequences


Lack of trust in the veracity (truthfulness) of the data

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Handling of ownership is challenging

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Asset operator

Component/system manufacturer

System Integrator

Stakeholder or others?

It is my data as it is generated in my processes

It is my data as it is generated from components I have manufactured

It is my data as it is aggregated in my control and monitoring systems

It is my data as it is aggregated a process where I hold the risk


New opportunities from cross industry data aggregation

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Better decisions with insights from data aggregates

Example OREDA:Eight Oil companies combining their failure data with the help of DNV GL as data curator since the eighties.

“The most Valuable dataset in the Oil & Gas Industry”


Data aggregation and cross asset analysis rely on standardisation

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Aggregation of data to cross industry benchmarks and indexes rely on standardisation.

An asset owner wanting a cross fleet perspective across systems and components from differnet vendors rely on standardisation

DNV GL are experts at developing, promoting, and using industry standards, domain and product models


Handling of privacy

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Asset operator

• I want to control who sees my data throughout the data value chain

• I want to compare against a benchmark, but I do not want my assets to be identified by others

• Etc.


The value of combined data sets – DNV GL Cross industries curated data lake

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YOUR DATA+

data from:>20% of world fleet

>65% of offshore pipelines>70% of offshore wind farms

>80,000 customersThousands of associated

suppliers>1 million software user

§ Shared data platform cost

§ Richer insights and analytics

§ Reduce friction

§ Benchmarking

§ Aggregate your data

§ Manage sharing of your data


VERACITY – Industry Insight Platform

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VERACITY – Industry Insihgt Platform

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SAFER, SMARTER, GREENER

www.dnvgl.com

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Bringing trust to the Internet of Things · 2017. 3. 13. · Sources Business value Volume (real time) Characteristics New Capabilities Behavioural data Velocity (real time) ... Plan