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Copyright © 2015 Rockwell Automation, Inc. All Rights Reserved.
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PUBLIC - 5058-CO900H
Industry 4.0 and Big Data
Marek Obitko, mobitko@ra.rockwell.com
Senior Research Engineer
03/25/2015
Copyright © 2015 Rockwell Automation, Inc. All Rights Reserved.PUBLIC
Background
2
Joint work with Czech Institute of Informatics, Robotics and Cybernetics
Big Data related topics investigated in RA-DIC laboratory within CIIRC
Goal of the effort: Semantic Big Data Historian
Copyright © 2015 Rockwell Automation, Inc. All Rights Reserved.PUBLIC
Agenda
3
Overview of related trends
Industry 4.0
Big Data
Semantics
Semantic Big Data Historian
Architecture
Use Case
Outlook
Conclusion
Copyright © 2015 Rockwell Automation, Inc. All Rights Reserved.PUBLIC
Agenda
4
Overview of related trends
Industry 4.0
Big Data
Semantics
Semantic Big Data Historian
Architecture
Use Case
Outlook
Conclusion
Copyright © 2015 Rockwell Automation, Inc. All Rights Reserved.PUBLIC
Industry 4.0
5
Fourth Industrial Revolution
Predicted a-priori, not observed ex-post
Economic impact predicted to be huge
Operational effectiveness, new business
models, services and products
Clear definition not provided
Usually: vision, basic technologies, selected scenarios
Design principles
Interoperability, virtualization, decentralization, real-time capability,
service orientation, and modularity
Copyright © 2015 Rockwell Automation, Inc. All Rights Reserved.PUBLIC
Industry 4.0 Components
6
Primary components
Cyber-physical systems Fusion of physical and virtual world – integration of computation and physical processes
Features: unique identification – RFID tags, centralized storage and analytics, multiple sensors and actuators, network compatible
Example: virtual battery – a battery in electric car has its virtual counterpart updated in real time, which allows diagnostics, simulation, prediction etc. for better customer experience
Internet of Things Network of physical systems that are uniquely identified and can interact to reach common goals
Example: Smart Homes – connected devices (temperature sensor, heating, mobile phone)
Internet of Services Offering services via Internet so that they can be offered and combined into value-added services
by various suppliers
Example: forming virtual production technologies and capabilities
Smart Factory – often mentioned as a key feature of Industry 4.0 Information coming from physical and virtual world used to provide context and assistance for
people and machines to execute their tasks in a better way
Example: demand driven production, intelligent work piece carriers
Other also related components: Smart product, Machine to machine (M2M), Big Data, Cloud
Copyright © 2015 Rockwell Automation, Inc. All Rights Reserved.PUBLIC
Agenda
7
Overview of related trends
Industry 4.0
Big Data
Semantics
Semantic Big Data Historian
Architecture
Use Case
Outlook
Conclusion
Copyright © 2015 Rockwell Automation, Inc. All Rights Reserved.PUBLIC
Big Data
8
Motivation
A CPG (consumer packaged goods)
company generates 5,000 data samples
every 33 milliseconds
This corresponds to 70TB per year
Can we meaningfully use such amount of data?
Big Data
… dataset that is growing so that it
becomes difficult to manage it using
existing database management
concepts and tools…
3Vs – Volume, Velocity, Variety
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Big Data
9
Volume – data will grow 50 times by 2020 – FB 50PB
Velocity – storing and getting data – fraud detection
Variety – unstructured, 90% of new data – videos
Applications
Online marketing – targeting products based onuser clickstream (Google, Amazon, Netflix…)
Medicine, biology, chemistry – data analysis
Technologies
Map-Reduce framework, introduced by Google
Running on cheap machines in parallel in clusters (splitting data) – implemented in e.g. Apache Hadoop
“It’s about variety, not volume”
The “Big” is not the main problem, focus on heterogeneous data integration –new analytic applications based on data that were not tracked so far
Copyright © 2015 Rockwell Automation, Inc. All Rights Reserved.PUBLIC
Agenda
10
Overview of related trends
Industry 4.0
Big Data
Semantics
Semantic Big Data Historian
Architecture
Use Case
Outlook
Conclusion
Copyright © 2015 Rockwell Automation, Inc. All Rights Reserved.PUBLIC
Semantics
11
Linked Data / Semantic Web (machine processable data)
Tens of RDF Gtriples on web
Resource Description Framework
Resources uniquely identified by URI
Triples subject – property – object
In fact – relations between objects, valuesof properties
Together forming RDF graph(s)
Web Ontology Language
Ontology – specifies the conceptualization
In fact – description of vocabulary, constraints, attaches meaning to identifiers
Designed for internet and web
And so also usable for Internet of Things,Internet of Services etc.
Inherently distributed approach, integration of data from heterogeneous and unreliable data sources
Copyright © 2015 Rockwell Automation, Inc. All Rights Reserved.PUBLIC
Agenda
12
Overview of related trends
Industry 4.0
Big Data
Semantics
Semantic Big Data Historian
Architecture
Use Case
Outlook
Conclusion
Copyright © 2015 Rockwell Automation, Inc. All Rights Reserved.PUBLIC
Plant Data Processing
13
Traditional Historian
Time series data collection, focus on fast scan rate
Analyzing data
“What the pH was at 2:34:56 PM March 15, 2015” Not a problem, single retrieval, unless there is
a problem with volume
“What the pH trend was from 1 to 7 PM of March 15, 2013, plus compare it to previous similar weekdays, holidays, after it rained, when different suppliers were used etc.”
Not easily possible in historians available today, especially for large scale data
Samples of needed data processing
Pattern recognition, pattern matching
Predictive maintenance
Benchmarking of KPIs
Clustering similar machines
Real time statistics / analytics / reporting
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Semantic Big Data Historian
14
Vision, currently being implemented to verify the technologies
Collecting data from sensors
Architecture based on OPC UA
Sensors semantically described
All data processed using Semantic Web languages andtechnologies – allows linkingdata together
Data stored in Hadoop
Analyzing data
Querying using SPARQL (RDF querying language)
More complex queries implemented directly in Map-Reduce framework
Copyright © 2015 Rockwell Automation, Inc. All Rights Reserved.PUBLIC
Description of sensors and data
15
Ontology building on top of SSN– “Semantic Sensor Network Ontology” (W3C effort)
Ontology describes
Sensors
Observations, includingphysical units, time,data quality etc.
Data expressed usingthe ontology
Particular observations
All data linked together
Directly stored asRDF triples
Copyright © 2015 Rockwell Automation, Inc. All Rights Reserved.PUBLIC
Agenda
16
Overview of related trends
Industry 4.0
Big Data
Semantics
Semantic Big Data Historian
Architecture
Use Case
Outlook
Conclusion
Copyright © 2015 Rockwell Automation, Inc. All Rights Reserved.PUBLIC
Case study – data from passive house
17
Our goal: evaluate the suitability of proposed
technologies, scalability etc.
Data focus: indoor air quality
Environmental parameters: Temperature,
Carbon dioxide concentration,
Relative humidity, Air pressure
Sample analysis tasks
Relaxation time of the house
Impact of sunlight on indoor
temperature
Detection of people inside
Copyright © 2015 Rockwell Automation, Inc. All Rights Reserved.PUBLIC
Case study – data from passive house
18
Raw data conversion to RDF to be stored to triple store
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Case study – data from passive house
19
Sample task – detection of people inside
Time series processing of CO2 data
Values in sliding window, comparing with threshold
Verified the results by comparing with people occupancy list
Main result
Data not really very big, however, reachingthe limits of MATLAB package
Map-Reduce implementation in Hadoop(both pre-processing and detection) much faster than in MATLAB
The task proved the advantage of Hadoopimplementation scalability
Copyright © 2015 Rockwell Automation, Inc. All Rights Reserved.PUBLIC
Agenda
20
Overview of related trends
Industry 4.0
Big Data
Semantics
Semantic Big Data Historian
Architecture
Use Case
Outlook
Conclusion
Copyright © 2015 Rockwell Automation, Inc. All Rights Reserved.PUBLIC
Outlook
21
Semantic Big Data Historian – overall goal:
Semantic: connect data together
Provide semantic description in the endpoints, connect to OPC UA and let the
Historian to connect the data appropriately
Big Data: be able to work with larger volume of data
Using Map-Reduce and similar frameworks to store, retrieve and analyze larger
volume of heterogeneous data
Historian
Focus on time-series data, however be able to also include other
types of data
E.g., information about suppliers, orders, shifts, various annotations etc.
Achieve analytics that was not possible without current technologies
Also connect to actions in physical world, not only ad-hoc analysis
Copyright © 2015 Rockwell Automation, Inc. All Rights Reserved.PUBLIC
Agenda
22
Overview of related trends
Industry 4.0
Big Data
Semantics
Semantic Big Data Historian
Architecture
Use Case
Outlook
Conclusion
Copyright © 2015 Rockwell Automation, Inc. All Rights Reserved.PUBLIC
Conclusion
23
Industry 4.0 – fusion of physical and virtual world,network of physical systems that interact to reachcommon goals, integration of services, smart devices, homes, factories, …
Big Data and Semantics – prerequisite forprocessing large volume of heterogeneous data
Semantic Big Data Historian
The goal is to provide advanced analytics on plant heterogeneous data, in the scale that was not possible until now
Demonstrated the Hadoop scalability
Demonstrated Semantic Web suitability for data integration
Next steps include advanced data analysis
Industry 4.0 – both distributed and centralized approaches needed
Small scale (M2M) versus large scale (cloud) data processing
Copyright © 2015 Rockwell Automation, Inc. All Rights Reserved.
PUBLIC
PUBLIC - 5058-CO900H
www.rockwellautomation.com
Thank you! Questions?
Contact: mobitko@ra.rockwell.com
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