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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, [email protected]

Senior Research Engineer

03/25/2015

mailto:[email protected]

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


Agenda

3

Overview of related trends

Industry 4.0

Big Data

Semantics

Semantic Big Data Historian

Architecture

Use Case

Outlook

Conclusion


Agenda

4


Industry 4.0

Big Data

Semantics


Architecture

Use Case

Outlook

Conclusion


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


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


Agenda

7


Industry 4.0

Big Data

Semantics


Architecture

Use Case

Outlook

Conclusion


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


Big Data

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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


Agenda

10


Industry 4.0

Big Data

Semantics


Architecture

Use Case

Outlook

Conclusion


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


Agenda

12


Industry 4.0

Big Data

Semantics


Architecture

Use Case

Outlook

Conclusion


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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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


Description of sensors and data

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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


Agenda

16


Industry 4.0

Big Data

Semantics


Architecture

Use Case

Outlook

Conclusion


Case study – data from passive house

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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



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Raw data conversion to RDF to be stored to triple store



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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


Agenda

20


Industry 4.0

Big Data

Semantics


Architecture

Use Case

Outlook

Conclusion


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


Agenda

22


Industry 4.0

Big Data

Semantics


Architecture

Use Case

Outlook

Conclusion


Conclusion

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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


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

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