Digital Transformation Convention 2017

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Janani Jayabal

Robotics, Big Data, KI and Intelligent Systems Industrial Data Intelligence - Echtzeit Einsicht in der Anlage

Data & Analytics

Industrial Data Intelligence
E c h t z e i t E i n s i c h t i n d e r A n l a g e
München, 24.10.2017
Peter Seeberg

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Edge
Production Analytics
Industrial Analytics
Self-Serving
Analytics
Autonomous Analytics
Manufacturing Analytics

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Machine Learning
On the Edge

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©Google,2016
©Amazon,2016

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Datenbasierte Produktionsoptimierung

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Self-Service -> Autonomous Analytics
Daten -> Algorithmus -> Entscheidung
Benutzer: Der Domain-Zuständige

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Algorithmus EntscheidungDaten
©PeterSeeberg/Softing,2016

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In 1959, Arthur Samuel
defined machine learning
as a "Field of study that…
gives computers the ability
to learn without being
explicitly programmed”

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©AndrewNg,2017

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©Google,2012

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Statistik…………
Data Mining….
Machine Learning
©GartnerGroup,2013
Beschreibende
Analyse
Diagnostische
Analyse
Vorausschauende
Analyse
Vorschreibende
Analyse
Was ist
Passiert?
Warum ist
es passiert?
Was wird
passieren?
Was ist
zu tun?

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Zeitreihen-Analyse
Anomalie-Erkennung

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{ x(t0), x(t1), … x(tn)}

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© "Anthony Bagnall, Jason Lines, William Vickers and Eamonn Keogh, The UEA & UCR Time Series Classification Repository,
www.timeseriesclassification.com"

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© "Anthony Bagnall, Jason Lines, William Vickers and Eamonn Keogh, The UEA & UCR Time Series Classification Repository,
www.timeseriesclassification.com"

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© "Anthony Bagnall, Jason Lines, William Vickers and Eamonn Keogh, The UEA & UCR Time Series Classification Repository,
www.timeseriesclassification.com"

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© "Anthony Bagnall, Jason Lines, William Vickers and Eamonn Keogh, The UEA & UCR Time Series Classification Repository,
www.timeseriesclassification.com"

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© "Anthony Bagnall, Jason Lines, William Vickers and Eamonn Keogh, The UEA & UCR Time Series Classification Repository,
www.timeseriesclassification.com"

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© "Anthony Bagnall, Jason Lines, William Vickers and Eamonn Keogh, The UEA & UCR Time Series Classification Repository,
www.timeseriesclassification.com"

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©ElizabethWeinbergforTheNewYorkTimes,2017
“The singer’s longevity in pop’s upper tier is “an
anomaly” owing to her consistency.”

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©ElizabethWeinbergforTheNewYorkTimes,2017
„…Massaker wie das von Las Vegas „Anomalien“ seien.
Die meisten Toten gebe es durch Morde und Suizide.“
©©REX/Shutterstock,2017

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©ElizabethWeinbergforTheNewYorkTimes,2017
“Elasticsearch machine learning will help
automatically detect and spot anomalies in data”

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©ElizabethWeinbergforTheNewYorkTimes,2017

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Datenbasierte Produktionsoptimierung
Gesamtanlageneffektivität (OEE)
• Verfügbarkeit

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Datenbasierte Produktionsoptimierung
Gesamtanlageneffektivität (OEE)
• Verfügbarkeit

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Datenbasierte Produktionsoptimierung
Gesamtanlageneffektivität (OEE)
• Verfügbarkeit
• Qualität
• Leistung

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Echtzeit-Einsicht in der Anlage
Plug & Play Datenanalyse mittels OPC UA
Acquisition Learning Scoring Visualization
G u i d e d S t r e a m A n a l y t i c s

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OSSA – On-Site Stream Analytics
EMO1

Folie 36
EMO1 Ein Microsoft Office-Anwender; 06.07.2017

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Datenbasierte Produktionsoptimierung
Gesamtanlageneffektivität (OEE)
• Verfügbarkeit

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Datenbasierte Produktionsoptimierung
Gesamtanlageneffektivität (OEE)
• Verfügbarkeit

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Datenbasierte Produktionsoptimierung
Gesamtanlageneffektivität (OEE)
• Verfügbarkeit

Copyright © 2016 Softing Industrial. All rights reserved. 41
Datenbasierte Produktionsoptimierung
Gesamtanlageneffektivität (OEE)
• Verfügbarkeit

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Algorithmus EntscheidungDaten
©PeterSeeberg/Softing,2016

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Peter Seeberg
Business Development Manager
Industrial Data Intelligence
Softing Industrial Automation GmbH
Vielen Dank für Ihre Aufmerksamkeit!
www.industrial.softing.com/de/IDI
@SoftingIDI

Similar to Digital Transformation Convention 2017

While the world still seems undecided on which terminology to use, the new industrial revolution has started and is already transforming the way we plan our future. Fuelled by the need to optimize the complete production lifecycle, make wiser use of natural resources, respond to fast-paced market changes, and level profitability across the globe, the new concept of Smart Factories was born. We see several research hubs, laboratories, and aggregators in action across the globe, where private, public, and academic entities are collaborating to define the guidelines, best practices, and use cases for the new smart manufacturing world. The topic is still hotly debated, but it has already defined some fundamental building blocks: - Telecommunication – for the estimated 500 connected devices for every person that are expected to exist by 2020, we need fast, secure, and widespread distribution of internet access points and datacenters - Big Data – for decision management platforms to expand at a foreseen CAGR of 60% through 2019, we need enhanced data processing and security algorithms - Security in the most pervasive way – being secure, vigilant, and resilient is the new black in the connected age The Industrial Internet Consortium (IIC) was founded in 2014 to promote the discussion between the leading vendors in IIoT and guide the future generation of intelligent manufacturing. In this presentation, Dr. Richard Soley, IIC Executive Director, and Oliver Winzenried, CEO and founder of Wibu-Systems, share: - an outlook on today’s and tomorrow’s manufacturing notion - the role of IIC in coordinating a vast ecosystem of initiatives - the surge of Smart Factories in the IIC agenda - the security implications of the rise of artificial intelligence in an industrial context - the reasons behind an endpoint security approach - the projects WIBU is engaged in for the development of secure smart factories - the turnkey technologies that are already available for safeguarding and monetizing the intellectual property of intelligent device manufacturers The new generation of embedded system developers is essentially in the same predicament that ISVs of traditional PC applications found themselves in years ago. Let’s openly share the state of the union and forge secure and connected supply chains together. Watch the webinar: https://youtu.be/Q-FNi1DVE5I

Smart Factories Redefine Security Paradigms

team-WIBU

Just add Imagination

KNIMESlides

@stefferber at Think Monk 2016: What is different in Internet of Things

Stefan Ferber

AppDynamics Browser RUM is a handy tool for getting insight into customers and their experience. In this session we'll explore aggregated data collected from AppDynamics' SaaS cloud that will give you insight into the end-user experience across the customer base. Then we'll show you how you can use end-user monitoring tools to see how you measure up against your peers and identify opportunities to optimize your performance. As an added bonus we'll share some data on you, AppDynamics users. It's always valuable to understand how you are like and not like your customers. Key takeaways: o The typical page load time of an end user across the AppDynamics customer base o How AppDynamics Browser RUM can be used to understand how your application performs now and make it go faster o The adoption of major browsers and devices of enterprise customers in different geographies vs. AppDynamics users For more information, go to: www.appdynamics.com

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VALUENEX held the following seminar in IPOS society, Singapore, on Sep. 13, 2016. This is the presentation slide of "How can we find the future on the radar map of big data?" session. # Seminar information "VALUENEX Presents: Utilizing Big Data for IP Innovation" https://www.eventbrite.sg/e/valuenex-presents-utilising-big-data-for-ip-innovation-registration-27210066040 With the expansion of ICT technologies and the growing library of databases, we now have easy access to valuable information. But with all the information received, it is in an unstructured format, with no clear organization and presentation. VALUENEX's "Panoramic View Analytics" aids us to find alternative solutions from the unexpected, and to innovate beyond what may seem possible.

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VALUENEX

The Industrial Internet of Things has set the stage for the convergence of Operations Technology (OT) and Information Technology (IT), that is, the plant floor and the higher-level IT infrastructure. One of the many aspects of this transitional journey is represented by M2M communications. OPC UA is a multi-platform, plug & play Information Exchange Standard for industrial smart automation and cloud networking. It standardizes communications within machines, between machines, and from machines to smart systems, securely networked with IoT architectures. As a member of the OPC Foundation, Wibu-Systems has been an early adopter of the OPC UA standard in Industrie 4.0 projects like IUNO, the German national reference project for IT security in Industrie 4.0, S4SmartPro, the key finder prototype production line of SmartFactoryKL, and OpSit, the optimal use of smart items technologies in healthcare. As recently pointed out in the Industrial Internet Security Framework as well, it is endpoints, i.e. the device or cloud-based components that have interfaces for network communication, that are particularly vulnerable in a world of cyber-physical systems connected to open networks. The Unified Automation ANSI C based and High Performance OPC UA SDKs, powered by CodeMeter Embedded, fully support the OPC UA defined Security Profiles and configurations and provide even stronger security for modern M2M communications. Secret information, like RSA private keys, certificates, and trust lists, is stored in a hardware secure element and protected from theft and tampering attacks. In a time when intellectual property is shifting in the value chain from hardware to software, manufacturers now also have new opportunities to capitalize on their software and offer feature-based, time-based, version-based, or pay-per-use models to scale up their offerings, expand their market share, and produce recurrent revenues. In this presentation, we are going to navigate you through a journey of exploration that will touch upon: * The elements of innovation in smart manufacturing * The connection requirements for M2M in the IIoT age * The building blocks of the OPC Unified Architecture * Use cases that are accelerating the rise of Smart Factories * The integration of CodeMeter in the OPC UA standard * The OPC UA security extension for endpoints Working with Windows, Linux, macOS, or Android? With minimal embedded controllers up to massive cloud infrastructures? OPC UA and CodeMeter are equally suited, scalable, and secure, and most of all integrated in a streamlined fashion to provide the ultimate technology in access control, authentication, and encryption. Watch the webinar: https://youtu.be/r3CHB42OJ-o

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The Process Industry has quite different requirements from Factory Automation. Generally, we are dealing devices that are exposed to the environment. Requires higher IP ratings. Hazardous environments (explosive atmospheres) are common. Requires Intrinsic Safety. We are often dealing with extensive systems with thousands of devices. Requires high availability. High speed operation is not normally required. Production cannot normally be stopped, so engineering activities such as maintenance and device replacement must have minimum impact.

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1. Industrial Data Intelligence E c h t z e i t E i n s i c h t i n d e r A n l a g e München, 24.10.2017 Peter Seeberg

11. Copyright © 2016 Softing Industrial. All rights reserved. 11 In 1959, Arthur Samuel defined machine learning as a "Field of study that… gives computers the ability to learn without being explicitly programmed”

15. Copyright © 2016 Softing Industrial. All rights reserved. 15 Statistik………… Data Mining…. Machine Learning ©GartnerGroup,2013 Beschreibende Analyse Diagnostische Analyse Vorausschauende Analyse Vorschreibende Analyse Was ist Passiert? Warum ist es passiert? Was wird passieren? Was ist zu tun?

18. Copyright © 2016 Softing Industrial. All rights reserved. 18 © "Anthony Bagnall, Jason Lines, William Vickers and Eamonn Keogh, The UEA & UCR Time Series Classification Repository, www.timeseriesclassification.com"

19. Copyright © 2016 Softing Industrial. All rights reserved. 19 © "Anthony Bagnall, Jason Lines, William Vickers and Eamonn Keogh, The UEA & UCR Time Series Classification Repository, www.timeseriesclassification.com"

20. Copyright © 2016 Softing Industrial. All rights reserved. 20 © "Anthony Bagnall, Jason Lines, William Vickers and Eamonn Keogh, The UEA & UCR Time Series Classification Repository, www.timeseriesclassification.com"

21. Copyright © 2016 Softing Industrial. All rights reserved. 21 © "Anthony Bagnall, Jason Lines, William Vickers and Eamonn Keogh, The UEA & UCR Time Series Classification Repository, www.timeseriesclassification.com"

22. Copyright © 2016 Softing Industrial. All rights reserved. 22 © "Anthony Bagnall, Jason Lines, William Vickers and Eamonn Keogh, The UEA & UCR Time Series Classification Repository, www.timeseriesclassification.com"

23. Copyright © 2016 Softing Industrial. All rights reserved. 23 © "Anthony Bagnall, Jason Lines, William Vickers and Eamonn Keogh, The UEA & UCR Time Series Classification Repository, www.timeseriesclassification.com"

25. Copyright © 2016 Softing Industrial. All rights reserved. 25 ©ElizabethWeinbergforTheNewYorkTimes,2017 „…Massaker wie das von Las Vegas „Anomalien“ seien. Die meisten Toten gebe es durch Morde und Suizide.“ ©©REX/Shutterstock,2017

35. Copyright © 2016 Softing Industrial. All rights reserved. 35 Echtzeit-Einsicht in der Anlage Plug & Play Datenanalyse mittels OPC UA Acquisition Learning Scoring Visualization G u i d e d S t r e a m A n a l y t i c s

37. Folie 36 EMO1 Ein Microsoft Office-Anwender; 06.07.2017

59. Copyright © 2016 Softing Industrial. All rights reserved. 58 Peter Seeberg Business Development Manager Industrial Data Intelligence Softing Industrial Automation GmbH Vielen Dank für Ihre Aufmerksamkeit! www.industrial.softing.com/de/IDI @SoftingIDI

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