Ron Hendriks is an IoT consultant and enthusiast who provides his contact information. The document discusses industrial automation and IoT, including typical hardware architectures, automation levels, protocols, and processes. It also discusses standards like ISA 88 and ISA 95. Challenges for implementing IoT in industrial automation are listed, as well as recent developments and opportunities like introducing more IT and open communication protocols. The near future and potential applications of machine learning are also mentioned.

1. Industrial
Automation
and
IoT
R. Hendriks

2. 2
Who am I?
Ron Hendriks
IoT Consultant
IoT Enthusiast
Linkedin: https://www.linkedin.com/in/ron-hendriks-8a03a87
Email: ron.hendriks@amis.nl
Phone: +31 (0)6 54226950
nlOUG Tech Experience 2018: People Counter

3. Components
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4. Tooling
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5. Typical hardware architecture
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6. Automation Levels
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7. Protocols
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8. Typical process
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9. ISA 88
Standard (philosophy) for Batch Control
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10. ISA 95
Standard for integration of control systems in Enterprises
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11. Automation Levels
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12. OEE
Overall Equipment Effectiveness
- Loading
- Availability
- Performance
- Quality
(Availability= 86.6%)*(Performance=93%)*(Quality=91.3%)= (OEE=73.6%)
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13. Challenges for IoT in Industrial Automation
• Lifecycle (>15 years)
• Robust, highly available
• Well supported
• Closed
• Diversity
• Incremental changes
• Small budgets
• High data intensity
• Security
• IoT trackrecord (“we don’t want our competitor to know”)
• USP IoT (“we already have that”)
• Maintenance staff
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14. Recent developements (opportunities)
Introduction of more IT
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15. Open communication protocols
Open Platform Communications Unified Architecture (OPC UA)
• Open standard (https://github.com/OPCFoundation/UA-.NETStandard)
• No more DCOM!! (OPC DA/AE wel)
• HTTP/HTTPS
• User administration
• Platform independent
• Extendable
• Subscriptions, events, methods
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16. Near future
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17. PID controller
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18. PID controller result
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19. Machine learning for optimization
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20. Machine learning for process prediction
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21. Machine learning for maintenance prediction
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22. Future transition
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