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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
DNV GL © 2017 20 January 2017
Fem forretningsområder
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OIL & GASMARITIME ENERGY BUSINESS ASSURANCE
SOFTWARE
DNV GL © 2017 20 January 2017
Digitalisation will impact even DNV GL
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DNV GL © 2017 20 January 2017
Digital disruptive forces
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DNV GL © 2017 20 January 2017
Target for disruptive digital initiatives
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Trapped value Transactional friction
DNV GL © 2017 20 January 2017
Industry is full of transactional friction and trapped value
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DNV GL © 2017 20 January 2017
The potential impact from digitally-enabled initiatives is high
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DNV GL © 2017 20 January 2017
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
DNV GL © 2017 20 January 2017
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
DNV GL © 2017 20 January 2017
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
DNV GL © 2017 20 January 2017
Managing risk in 1864
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DNV GL © 2017 20 January 2017
Analogue trust
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DNV GL © 2017 20 January 2017
Industry 4.0 - Toward data-driven decision making
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Manufacturer
Operator
Authority
Asset owner
Service provider
Systems integrator
DNV GL © 2017 20 January 2017
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
DNV GL © 2017 20 January 2017
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
DNV GL © 2017 20 January 2017
But – You do not want to end up here
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DNV GL © 2017 20 January 2017
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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DNV GL © 2017 20 January 2017
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
DNV GL © 2017 20 January 2017
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:
DNV GL © 2017 20 January 2017
Invest in new technologies - Azure Cloud Data Management platform capabilities
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DNV GL © 2017 20 January 2017
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.
DNV GL © 2017 20 January 2017
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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DNV GL © 2017 20 January 2017
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,++).
DNV GL © 2017 20 January 2017
Digital trust – challenges and opportunities
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DNV GL © 2017 20 January 2017
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
DNV GL © 2017 20 January 2017
Lack of trust in the veracity (truthfulness) of the data
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DNV GL © 2017 20 January 2017
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
DNV GL © 2017 20 January 2017
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”
DNV GL © 2017 20 January 2017
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
DNV GL © 2017 20 January 2017
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.
DNV GL © 2017 20 January 2017
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
DNV GL © 2017 20 January 2017
VERACITY – Industry Insight Platform
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DNV GL © 2017 20 January 2017
VERACITY – Industry Insihgt Platform
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DNV GL © 2017 20 January 2017
SAFER, SMARTER, GREENER
www.dnvgl.com
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