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Sinalytics enables Digitalization:Industrial Data AnalyticsDr. Mikhail Roshchin, Head of Diagnostic Portfolio
Siemens Corporate TechnologyUnrestricted @ Siemens AG 2016
Unrestricted © Siemens AG 2016June 2016Page 2 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
Unrestricted © Siemens AG 2016June 2016Page 3 Corporate Technology
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
Unrestricted © Siemens AG 2016June 2016Page 4 Corporate Technology
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
Unrestricted © Siemens AG 2016June 2016Page 5 Corporate Technology
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
Unrestricted © Siemens AG 2016June 2016Page 6 Corporate Technology
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
Unrestricted © Siemens AG 2016June 2016Page 7 Corporate Technology
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
Unrestricted © Siemens AG 2016June 2016Page 8 Corporate Technology
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
Unrestricted © Siemens AG 2016June 2016Page 9 Corporate Technology
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
Unrestricted © Siemens AG 2016June 2016Page 10 Corporate Technology
…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
Unrestricted © Siemens AG 2016June 2016Page 11 Corporate Technology
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
Unrestricted © Siemens AG 2016June 2016Page 12 Corporate Technology
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
Unrestricted © Siemens AG 2016June 2016Page 13 Corporate Technology
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
Unrestricted © Siemens AG 2016June 2016Page 14 Corporate Technology
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
Unrestricted © Siemens AG 2016June 2016Page 15 Corporate Technology
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
Unrestricted © Siemens AG 2016June 2016Page 16 Corporate Technology
• 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
Unrestricted © Siemens AG 2016June 2016Page 17 Corporate Technology
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
Unrestricted © Siemens AG 2016June 2016Page 18 Corporate Technology
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
Unrestricted © Siemens AG 2016June 2016Page 19 Corporate Technology
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
Unrestricted © Siemens AG 2016June 2016Page 20 Corporate Technology
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
Unrestricted © Siemens AG 2016June 2016Page 21 Corporate Technology
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
Unrestricted © Siemens AG 2016June 2016Page 22 Corporate Technology
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
Unrestricted © Siemens AG 2016June 2016Page 23 Corporate Technology
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
Unrestricted © Siemens AG 2016June 2016Page 24 Corporate Technology
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
Unrestricted © Siemens AG 2016June 2016Page 25 Corporate Technology
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
Unrestricted © Siemens AG 2016June 2016Page 26 Corporate Technology
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
hypothetical identifiers
hypothetical identifiers
Unrestricted © Siemens AG 2016June 2016Page 27 Corporate Technology
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/
Unrestricted © Siemens AG 2016June 2016Page 28 Corporate Technology
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,
site con-figurations,
measurable
quantities,
Processes
Dom
ain
onto
logy
Sem
antic
map
ping
examples
* FP7-318338 - http://optique-project.eu/
Unrestricted © Siemens AG 2016June 2016Page 29 Corporate Technology
Images + Text
Inspections Observation sheet(hypothetical)
Digipen
Free-text Docs
NLPOCR Indexing
NLP &Deep
LearningIndexing
Query
Analytics
Normalstart?Sensor types,
turbinestructure,
site con-figurations,
measurable
quantities,
Processes
Dom
ain
onto
logy
Sem
antic
map
ping
examples
Remote monitoring example:Semantics in the wider ecosystem
* FP7-318338 - http://optique-project.eu/
Abstraction enables uniform solutions (EU funded Project Optique*)B
Unrestricted © Siemens AG 2016June 2016Page 30 Corporate Technology
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
Unrestricted © Siemens AG 2016June 2016Page 31 Corporate Technology
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
Unrestricted © Siemens AG 2016June 2016Page 32 Corporate Technology
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
Unrestricted © Siemens AG 2016June 2016Page 33 Corporate Technology
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
Unrestricted © Siemens AG 2016June 2016Page 34 Corporate Technology
Web of Systems technologies already work –Example: The Intelligent Secondary Substation in a Smart Grid
Unrestricted © Siemens AG 2016June 2016Page 35 Corporate Technology
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