Sinalytics enables Digitalization: Industrial Data Analytics › event › 524996 › contributions...

Sinalytics enables Digitalization:Industrial Data AnalyticsDr. Mikhail Roshchin, Head of Diagnostic Portfolio

Siemens Corporate TechnologyUnrestricted @ Siemens AG 2016

Unrestricted © Siemens AG 2016June 2016 Corporate Technology

Demographic change

Urbanization

Climate change

Globalization

Five Megatrends shaping our world of tomorrow –changes in the markets are accelerating

1 UN World Population Prospects (2015)2 Met Office Hadley Centre observations (2014)

3 McKinsey Global Institute Cityscope (2011)4 UNCTAD (2013)

5 Cisco: The Internet of Everything (2013)6 IDC: The Digital Universe (2012)

Digital transformation

Trend to increaseinvestment abroad

Growing and ageingpopulation

Cities as main driverof GDP growth

Global warming andweather extremes

Contribution to global GDP growth,2007-20253, in %

Foreign direct investmentvs. global GDP, in %4

World population1, in bn Annual mean temperaturevariations 1950-20142 (in °C)

Connected devices5, in bn

-0,3

0

0,3

1950 1970 1990 2010

0.3

-0.3

0

2

4

6

1970 1976 1982 1988 1994 2000 2006 20122013

Other citiesand rural areas38%

Cities >10mn

5mn – 10mn

2mn – 5mn

150k – 2mn

50

239

2012 20202016

Exponentialgrowth ofconnecteddevices ...

… anddigital data6

2.8 ZB40.0ZBZB = Zettabytes = 109 Terabytes

9.000.16

1.92 0.836.10

0.12

2015

1.25 -0.010.00 1.36

2050

Developingcountries

Industrialcountries 15-65

65+

0-14

Age


Digitally enhancedelectrificationand automation

#1 automation player in industry,buildings, grids, power plants, and rail

€0.6 billion revenue in FY15

+15% yoy growth FY15

>300k devices– Remotely monitored and administered– Data driven– Analytics enabled

Digitalservices

€3.1 billion revenue in FY15

+16% yoy growth FY15

Leading provideracross verticals

Verticalsoftware

Customer benefit translates to substantialrevenue growth for Siemens – Siemens Digitalization

XHQ


Siemens Digitalization

Siemens Digitalization –Leveraging digital technology trends for concrete customer benefits

Connectivity andWeb-of-systems

Collaborationand mobile

Smart data andanalytics

Cloudtechnologies

Cyber-Security

Maintenance &services

Automation &operation

Design &engineering

Improved productivity &time-to-market

Higher flexibility &resilience

Increased availability &efficiency

Combining the virtual & physical world …… across entire customer value chains


Siemens Digital Services powered by Sinalytics – Combiningtechnology with domain and context know-how for customer value

Context of data from installed basis

Installedproducts,systems,processesand sensors

Domainknow-how

+Context

know-how

+Analyticsknow-how

− Improved performance

− Energy savings

− Cost reductions

− Risk minimization

− Quality improvement

= Digital Servicespowered by Sinalytics

Customer valueOptions for actionInformationData analysisData


Sinalytics: Our new platform for data-based services

We build on common technology platforms …+ Latest technology for all Siemens businesses+ Reduction of technical complexity in the company+ Leveraging synergies through scaling+ Faster development

… and use the customer proximity of ouroperating units to develop applications

+ Know-how regarding large installed bases of productsand systems

+ Deep know-how of customer processes and challenges+ Many existing applications that already generate value

for our customers

ProcessIndustries& Drives

DigitalFactory

EnergyManagement

Healthcare

Power and Gas,Power Generation

Services

Wind Power

Mobility

BuildingTechnologies

SinalyticsData analyticsData visualization

Modeling/Analysis

Data management

Data integration

Cloud/ConnectivityCyber Security

Availability Security

Productivity Energy efficiency


The Digital Transformation of Services

Improving efficiency of classical services

Performance & Outcomebased Contracting

Classical Time &Material Maintenance

NetworkPlatforms

E.g. Digitaldiagnostics

E.g. AugmentedReality for trainingand MaintenanceRepair andOverhaul

E.g. digitallyoptimised workscheduling


The Digital Transformation of Services

1. Predictive Maintenance 2. Performance-based contracts 3. Outcome-oriented contracts

Performance & Outcomebased Contracting

Classical Time &Material Maintenance

NetworkPlatforms

E.g. Healthcare E.g. Power ServicesE.g. Mobility

Power Services

ObjectivesImprove customerROI through flexibleservice in anymarket condition

ObjectivesPrevent unplanneddowntime of CTscanners causedby tube failures

ObjectivesIncrease availa-bility and reliabilityof trains

PreventiveMaintenance

ReactiveMaintenance

Condition-basedMaintenance

PredictiveMaintenance

PrescriptiveMaintenance


Sinalytics Platform powers Siemens Digital Services

Connectivity

300,000+connected devicesincluding wind turbines,buildings, trains

Advanced analyticsGreeting new insightsfrom smart data byleveraging world-classtechnologies

Customer outcomes─ Higher availability─ Lower costs─ Increased

performance─ More security

Know-how─ Product, domain and

data science know-how─ Overall IT know-how

with about17,500software engineers

─ 160 Data Scientists


…EnergyIPMindsphere

Sinalytics builds on a strong technology stack for connectivity,data analytics supported by state-of-the-art cyber security

Variety of data sources and types~300,000 devices cRSP, ISB ...

BTDF PD PG PS WP EM MO HC

Data analysis

Data integration

Data management

Data presentation

On premise Cloud

Usecase 1

Usecase 2 … … … Use

case n

Customizable analytics solution packages

Sinalytics

BU specific use case implementations

Common industrial data analytics platform‒ Several off-the-shelf tools are available in each layer‒ Tools are integrated with each other through “glue code”‒ On-premise and cloud deployment possible

Common data analytics set-up‒ Modular analytics solution packages

Connectivity‒ Build on reliable and secure solutions

e.g. common Remote Service Platform

Cyb

erse

curit

yso

lutio

ns

Industry specific Applications/Platforms

Siemens Digital Services


Siemens Digital Services powered by Sinalytics –Example: Predictive maintenance of trains and locomotives

Results

Improved assetavailability

Avoidance ofunnecessarymaintenance

Reduction ofmaintenance costs

Rail Transport– Market drivers– Rail operator challenges– Rail user demands

Trains/Locomotives– Rail vehicle engineering– Mechanical vibrations– Sensor properties– Maintenance operations

Data Science– Pattern identification– Machine learning– Automated alert generation

Domainknow-how

Contextknow-how

Customervalue

Analyticsknow-how

Siemens


Smart data to business example:Health check for CERN’s Large Hadron Collider

Automation infrastruct.

• Market leader in industryautomation

• Strong presence in allbusiness areas

Autom. components

• Complex: hundreds of SCADAsystems and SIMATIC controlsystems

Rule and pattern mining

• Several GB of log datagenerated per day

• Detect fault patterns

Results

Early warnings toincreaseOperating Hours

Domainknow-how

Contextknow-how

Customervalue

Analyticsknow-how

Siemens


Siemens Digital Services powered by Sinalytics –Example: Optimization of gas turbine operations

Results

Reduced NOxEmissions

Extension ofservice intervals

Energy System– Market drivers– Customer needs– Product cycles

Gas Turbines– Mechanical Engineering– Thermodynamics– Combustion chemistry– Sensor properties

Autonomous Learning– Neural Networks &

Reinforcement Learning– Smart Data Architecture

processes data from 5,000sensors per second

Domainknow-how

Contextknow-how

Customervalue

Analyticsknow-how

Siemens


We continue developing data analytics capabilities of Sinalytics:From predictive to prescriptive analytics

Same functional architecture

Predictive analytics for service

On-premiseand in the cloud

“Data and models at rest”

Streaming analytics and

for decision support

On-premise, in the cloudand in the field

“Data and models in motion”

Operationalprescriptive analytics

On-premise, in the cloudand in the field

“Automated data analytics”

Wave 1 Wave 2 Wave 3

distributed analyticsUsing Webof Systemstechnologies


The overarching trend: The cognitive computing era is coined throughartificially intelligent systems

1997: IBM Deep Blue defeatsworld’s chess championKasparov

2012: Watson wins Jeopardy!against most successfulcontestants

1946: Zuse’s Z3, firstprogrammable electroniccomputer

1962: First industrial robotUnimate at General Motors

2005: Honda's humanoidrobot Asimo comes to life

1969: Shakey is the firstrobot that could reason aboutits surrounding

Tabulating System EraProgrammable System Era

Cognitive System Era

today

1987: Mac IIHome Computer

Computationalspeed

1 Exaflop

1 Petaflop

1 Teraflop

1 Gigaflop

2012: iPad 4

2012: IBMWatson

1997: IBM Deep Blue

2013: Tianhe-2, world‘s fastestsupercomputer

20xx: SW systems passTuring Test repeatedly andreliably


• Classification

• Regression

• Forecasting

• Controlling

• Optimization

• Perception

• Cognition

• Decision

Decision/Action-orientedperspective:

Analytics/Method-orientedperspective:

Artificial Intelligence encompasses a variety oftechnologies – integration is crucial

Reasoning

NLP

Speech

PlanningOptimization

Agent technologies

KnowledgeRepresentation

(e.g., Knowledge graph/ Ontology)

Machine Learning

Representation Learning(e.g., shallow auto-encoder)

Deep Learning(RNN/CNN)

ReinforcementLearning

Vision

Sensor


Giving birth to an AI Renaissance - AI is the science and engineering ofmaking intelligent machines

Memory(knowledge representation)

Text Processing

Speech recognition

Image Processing

Sensor Processing Reasoningà Draw conclusions

Learningàadapt & improve

Decision making(also in uncertainty)

Perception Cognition Decision

Creativityàgeneratehypotheses

ActionEnvironment

AB

C

Semantic knowledge fusion and reasoningfor integrated diagnostics

Automated planning of maintenanceservice and activities

Deep learning for object recognition andlabeling in service reports

B

C

Remote diagnosticcenters

Global datacenters

• Monitoring• Root cause analysis• Predictive maintenance• Reports• Global service products

Example Device Service Automation

A


Advances in Deep Learning drives as enabling technology the wave ofAI research and application within Text / Speech and Computer Vision

Artificial Intelligence

Recurrent Neural Network

Deep LearningConvolutional Neural Network

Representation Learning

IBM Watson LeCun/Ng

Her (film)G. HintonImage Net Google-Car

Ng/StanfordParallel-NN/GPU

Y. BengioGPU-based NN

(Oh and Jung, 2004)

Frequency of updates of wikipedia pages:

A


Since 2010, significant developments in the Deep Learning havetriggered a unique momentum

GPUs, fasterconnectivityand better infra-structure fordistributedcomputing, isone of the mostimportant trendsin deep learning

• State of the art toolkits &technologies pushed to open source

• The GitHub-Momentum asshared code-base incl. models

• Interdisciplinary approachcombining vision and nlp domain

• Challenges (e.g. ImageNet) triggerbroad participation and visibility

Usage of hugedatasets (esp. fortraining data)commences toleverage fullpotential ofdeep learning

Image recognitionand object classifi-cation KPIs haveexperienced aan annual improve-ment of ~30%since 2011, mainlydue to CNNs

Increasing model size (complexity)Increasing Dataset sizes

Increasingly open ecosystemsIncreasing accuracy

(>105 entries insize of trainingdata set) ˄(younger than 2010)

(>106 neuronsper network) ˄(youngerthan 2010)

[Source: http://www.iro.umontreal.ca/~bengioy/dlbook/intro.html, 2016]

A


Deep & Representation Learning: Success Stories in Computer visionand NLP. Industrial applications coming

Computer Vision

• Image classification[Krizhevsky et al. (‘12), Ciresan et al. (‘12), Gong et al. (‘13)]

• Object detection[Sermanet et al. (‘13), Szegedy et al. (‘14)]

• Image caption generation[Kiros et al. (‘14), Fang et al. (‘14), Karpathy et al. (‘14), Xu et al. (‘15)]

• Image clustering[Torralba et al. (‘08), Taigman et al. (‘14)]

• Handwriting recognition[Ciresan et al. ('10)]

Natural Language Processing

• Word/sentence/document representation learning & information retrieval[Socher et al. ('11), Mikolov et al. ('13), Le et al. ('14), Shen et al. ('14)]

• End-to-end machine translation (e.g. English ® French)[Cho et al. ('14), Bahdanau et al. ('14), Sutskever et al. ('14)]

• Named entity recognition, semantic role labeling, etc. [Collobert et al. ('11)]

• Sentence/document classification, sentiment analysis[Maas et al. ('11), Socher et al. ('13), Lai et al. ('14), Zhao et al. ('14)]

• Acoustic speech recognition (e.g. Google, Siri, Cortana)[Hinton et al. ('12), Deng et al. ('13), Graves et al. ('13), Maas et al. ('13)]

DeepFace @

Google Now @

Siri @

Cortana @

Inception network

A


Multimodal Recurrent Neural Architecture: Deep Visual-SemanticAlignments for Generating Image Descriptions*

Deep Visual-Semantic Alignments• Alignment model is based on a combination of

Convolutional Neural Networks (VGG-16) overimage regions, bidirectional Recurrent NeuralNetworks over sentences, and a structured objectivethat aligns the two modalities through a multimodalembedding.

• Multimodal Recurrent Neural Network architecturethat uses the inferred alignments to learn togenerate novel descriptions of image regions

• For each image, the model retrieves the mostcompatible sentence and grounds its pieces in theimage

*Andrej Karpathy, Li Fei-Fei (2015): Deep Visual-Semantic Alignments for Generating Image Descriptions, CVPR

http://cs.stanford.edu/people/karpathy/cvpr2015.pdf

A


Deep Visual-Semantic Alignments for Generating Image Descriptionsand its application to Power Service Outage Reports

Basket

Swirlers

Swirler cups

A

“typical view of center swirler cup”

Source: http://www.energy.siemens.com/co/pool/hq/energy-topics/pdfs/en/gas-turbines-power-plants/9_SGT65000F.pdf


Deep Visual-Semantic Alignments for Generating ImageDescriptions and its application to Outage Reports

“A man and a woman riding an elephant” “Turbine blade with coating damage”Hypothetical, no damage on marked location

A

Source: http://www.energy.siemens.com/co/pool/hq/energy-topics/pdfs/en/gas-turbines-power-plants/9_SGT65000F.pdf


Connecting industrial knowledge (sources)B

Relational Learning (e.g. via Tensor Factorization)

Pattern Sequence Mining (e.g. via PrefixSpan)

A

ROOT

ABAA

AAA AAB AAC ABA ABB

Not frequent

Not frequent

Build in-depth prefixes

Data Sources Industrial Knowledge Graph

R&Ddata

Engineeringdata

Plantdata

Servicedata

Monitoringdata

Static aspects

Dynamic aspects

Examples for automated graph construction

Information extraction(e.g. Natural Language Processing)

Tresp, Nickel. Tensor Factorization for Multi-Relational Learning, ECML, 2013

Knowledge fusion into one coherent semantic model


Enable intuitive end-user access to industrial dataB

“Industrial Knowledge Graph”

R&Ddata

Engineeringdata

Plantdata

Servicedata

Monitoringdata

Static aspects

Dynamic aspects

Redesign necessary(Copy from internet)

Industrial Knowledge GraphData Sources

Query

Analytics

Normalstart?

Ontology-based Data Access

examples


Semantic knowledge fusion and reasoning for integrated diagnostics

BSX-TC3562-XE01

BSX-TMP12A-XE01

BSX-TICCFB1-XE01

MS-XC255-X12

BSX-TC3562-XE01

BSX-TC3562-XE01

MRR-T8901-8462

CRR-M8393-9272

“Ignitor on”

DC1, DB X1, TY2

DC2, DB X2, TY2

DC2, DB X2, TY2

DC2, DB X2, TY4

Query1

Query2

Query3

Normalstart ?

Normalstart ?

Normalstart ?

B

hypothetical identifiers




Query

Abstraction enables uniform solutions (EU funded Project Optique*)

BSX-TC3562-XE01

BSX-TMP12A-XE01

BSX-TICCFB1-XE01

MS-XC255-X12

BSX-TC3562-XE01

BSX-TC3562-XE01

MRR-T8901-8462

CRR-M8393-9272

“Ignitor on”

Analytics

Normalstart?Sensor types,

turbinestructure,

site con-figurations,

measurable

quantities,

Processes

Dom

ain

onto

logy

Sem

antic

map

ping

B

examples

* FP7-318338 - http://optique-project.eu/


Query

Remote monitoring example:Abstraction enables uniform solutions (Optique)

Analytics

Normalstart?

BSX-TC3562-XE01

BSX-TMP12A-XE01

BSX-TICCFB1-XE01

MS-XC255-X12

BSX-TC3562-XE01

BSX-TC3562-XE01

MRR-T8901-8462

CRR-M8393-9272

“Ignitor on”

Abstraction enables uniform solutions (EU funded Project Optique*)B

Sensor types,

turbinestructure,


measurable

quantities,

Processes

Dom

ain

onto

logy

Sem

antic

map

ping

examples



Images + Text

Inspections Observation sheet(hypothetical)

Digipen

Free-text Docs

NLPOCR Indexing

NLP &Deep

LearningIndexing

Query

Analytics

Normalstart?Sensor types,

turbinestructure,


measurable

quantities,

Processes

Dom

ain

onto

logy

Sem

antic

map

ping

examples

Remote monitoring example:Semantics in the wider ecosystem


Abstraction enables uniform solutions (EU funded Project Optique*)B


Knowledge fusion enhances diagnostics capabilities

Blade damagePrevious inspection

Monitoring

Configuration data

Incident report(hypothetical)

Engineering data

Diagnostics example (hypothetical)1. Monitoring: unusually high vibration2. Monitoring: lubrication oil system normal3. Service: history of damaged blades4. Configuration: no salt-water exposure5. Hypothesis: blade damage due to high thermodynamic load6. Common cause: blade cooling malfunction7. Common cause: cooling vent blocking8. Common cause: air intake filter failure

Suggested root cause:à Air intake filter degradation or damage

Example: vibration

B


Service Activity Planning

From unified insights to actions

Scheduling of actionsSequencing of actionsDerive required actions

Reduced time for serviceengineers

Higher flexibility &resilience

Increased availability &efficiency

C

§ Order/ship air intake filter§ Check air intake filter§ If damaged: replace air intake filter§ If negative: aerodynamic wash

If problem persists: Consider fullinspection

Query required actions“Air intake filter degradation or damage”

1234

1

2 3 4‘

5Negative?Damaged?

Classical AI Planning & ReasoningEvaluate pre-/postconditiondescribed in ontology

4

4‘ Inferred action: Shutdown

5

5‘Problem persists

OptimizationMinimize service costs and downtimesconsidering side constraints / sequences

5‘

1

2

3

4

5ScheduledDowntime

Wiki / Dashboard

Diagnostics

Service Planning

IndustrialKnowledge Graph


Summary: Focus of data analytics is changing from description of past todecision support and autonomy

Value and Complexity

Inform

Analyze

Act

Descriptive

Examples• Plant operation report• Fault report

What happened?

Diagnostic

• Alarm management• Root cause

identification

Why did it happen?

Predictive

• Power consumptionprediction

• Fault prediction

What will happen?

Prescriptive

• Operation pointoptimization

• Load balancing

What shall we do?

Siemens


Sinalytics brings together the technologies neededin an increasingly digitalized world

Energymanagement

Powergeneration

Energyconsumers

IQ

IQ

IQ

IQIQ IQ

Data analyticsOn-premise, in the cloud andsoon in-the-field leveragingWeb of Systems technologies

Cyber securityProtecting customer data inopen, interconnected industrialIT systems

ConnectivitySecure and proventechnologies connectingalready ~300k devices

Smart networkeddevices & systemsSystem-aware, autonomousand app-enabled to meetindustry and infrastructureneeds

Sinalytics


Web of Systems technologies already work –Example: The Intelligent Secondary Substation in a Smart Grid


Questions and Answers

Connectivity

300,000+connected devicesincluding wind turbines,buildings, trains

Advanced analyticsGreeting new insightsfrom smart data byleveraging world-classtechnologies

Customer outcomes─ Higher availability─ Lower costs─ Increased

performance─ More security

Know-how─ Product, domain and

data science know-how─ Overall IT know-how

with about17,500software engineers

─ 180 Data Scientists

Sinalytics enables Digitalization: Industrial Data Analytics › event › 524996 › contributions...

Documents

Transcript of Sinalytics enables Digitalization: Industrial Data Analytics › event › 524996 › contributions...