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DIGITAL AUTOMATION IIOT PLATFORM FOR INDUSTRY 4.0 – SMART AUTOMATION
Assoc.Prof.Dr Teerawat Thepmanee Automation Engineering
KMITL
2 Choice of the Day
Digital Transformation
Automation Architecture
Data – Driven Smart Manufacturing
Digital Automation IIoT Platform for Industry 4.0 - Smart Automation
3
Artificial Intelligence
Internet of Things Cyber Physical
Systems
Digital Twin
Digital wave
Digital…
Digital Transformation 4
Why do we need to transform?
o What is digital transformation?
o Why does digital transformation matter?
o What does a digital transformation framework look like?
o What role does culture play in digital transformation?
o What drives digital transformation?
o How can I get started on digital transformation?
o Where can I learn more?
What do we need to transform?
How can we transform?
Why do we need to transform? : Digital Wave 5
o The Industrial Revolution: The real one and more or less a combination of the first and second revolution in the Industry 4.0 view.
o The Internet Revolution: computing power and the rise of distributed information networks. o The Industrial Internet: what is called the fourth industrial revolution in Industry 4.0.
What do we need to transform? : Industry 6
Digitalization in Process Control and Automation system 7
Digitalization from ERP to Intelligent ERP 8
How can we transform? : Framework 9
“Digital transformation as the integration of digital technology into all areas of a business resulting in fundamental changes to how businesses operate and how they deliver value to customers”
Digital Transformation : Core Technology 10
o Enterprise-Control System Integration Model o Hierarchical Architecture of Production System o Lifecycle Management of Product, Production and Production Assets o Intelligent Device Model o Electronic Device and System Structure Description o Digital Communication (Fieldbus) Technology o Digital Integration Technology o Cyber Security and Control System Security
Digital Transformation : From Disruptive to Productive 11 How Innovative Technology Trends are Powerfully Enhancing Workforce Productivity
o Today’s Technology Mega-Trends
o Major Market Shifts o Manufacturing Technology
Enablers o Manufacturing Productivity
Pain Points o Disruptive Manufacturing
Trends o Productivity Benefits of
MES
o The technology has been in use since 1960s.
o NASA has been creating physically duplicate systems for its various space mission at ground level to test its equipment in a virtual environment.
o An example to this is Apollo 13 for which a Digital Twin was developed by NASA to assess and simulate conditions on board.
o Digital Twins enables in-depth analyses by leveraging big data, IoT and AI solutions.
o It is very useful in detecting potential issues, preventing downtime and testing new business opportunities, plan
Digital Transformation : Digital Twin 12
13
Industrial automation : Production 14
Raw materials
• Suppliers
Work in process
• Manufacturing
Finished goods products
• Customers
Information flows
Industrial automation : Production 15
Information flows
Industrial automation : System Integration 16
LEVEL 1 Measurement and Control Devices (I/O devices e.g. Sensors, Actuators)
LEVEL 2 Basic Process Control System (Monitoring, Controlling Process)
LEVEL 3 Manufacturing Operation Management (Production, Maintenance, Quality and Inventory)
LEVEL 4 Enterprise Resource Planning (Business planning & Logistics)
LEVEL 0
Batch, Continuous, Discrete
ISA-95: The International Society of Automation
IEC 62264: International Electrotechnical Commission
Industrial automation : Industrial Revolution 17
Industrial automation : Expectations of automation 18
• Process Optimisation – Energy, material and time savings – Quality improvement and stabilisation – Reduction of waste, pollution control – Compliance with regulations and laws, product
tracking – Increase availability, safety – Fast response to market – Connection to management and accounting (SAP™) – Acquisition of large number of “Process Variables”,
data mining
Ref. Prof. Dr. H. Kirrmann EPFL / ABB Research Center, Baden, Switzerland
Industrial automation : Expectations of automation 19
• Asset Optimisation – Automation of engineering, commissioning and maintenance – Software configuration, back-up and
versioning – Life-cycle control – Maintenance support – Engineering Tools
• Personal costs reduction – Simplify interface – Assist decision – Require data processing, displays,
data base, expert systems – Human-Machine Interface (MMC=Man-Machine Communication)
Ref. Prof. Dr. H. Kirrmann EPFL / ABB Research Center, Baden, Switzerland
Industrial automation : Industry Challenges 20
Manage data overload and turn into knowledge for a safer, more efficient and reliable plant
Fewer skilled people must respond faster, handle more complex processes, make better decisions, with bigger consequences across the global enterprise… …as we address these trends, we create more data to manage…
Data overload Skilled worker shortages High technology churn in open systems Safety & security concerns Economic swings driving unpredictable demand
loads Increasing regulations Environmental concerns
Industry 3.0 vs. Industry 4.0
• Cyber Physical Systems (non existent in Industry 3.0) • Interoperability (not provided in Industry 3.0) • Automation Pyramid (does not exist anymore in Industry 4.0)
Yesterday and Today Future
21
Industrial Internet of Things (IIoT) 22
o What is IIoT and Impact on Manufacturing? o What are the Benefits of IIoT?
4 Key Trends in Automation 23
1. Smart Everything
4 Key Trends in Automation 24
2. Enterprise-Control System Integration
4 Key Trends in Automation 25
3. Robots and Robotic Process Automation
4 Key Trends in Automation
• Use of IoT technology to connect everything in a plant: machines, equipment, MES, ERP and the produced good itself.
26 4. IoT drives Industry 4.0
27
Data – Driven Smart Manufacturing
Data – Driven Smart Manufacturing 28
It al
l sta
rts
here
: Field Devices
provide data that feeds the Information Model
into this…..
and even this…
Connect Everywhere
YOU
CAN
One Device - One Package - All Tools
Data infrastructure : IoT Cloud Platform 33
Model and Information Integration FDI: IEC 62769
Data infrastructure : IoT Cloud Platform 34
Edge Programmable Industrial Controller
(Edge Programmable Industrial Controller)
Data infrastructure : IoT Cloud Platform 35
Open Source IoT Gateway
Data infrastructure : IoT Cloud Platform 36
Cloud and Edge computing
Data Analytics : Application 37
o IIoT for Predictive Maintenance Program o Extending Operational services into IIoT Solution Managed Services o Building the right ‘Failure Model’
Data Analytics : Operational services into IIoT Solution 38
Data Analytics : Types of Data Analytics 39
Data Analytics : Methods 40
Data Analytics : DIKW Process 41
Smart Devices
BPCS (PA, FA) , Automatic Machine, Robotic, or infra structure etc.
Theory or/and experience knowing the right way to do thing
Applying to knowledge to improve practice (Analytics Software)
Data Analytics : Ex. DIKW Process 42
Data – Driven Smart Manufacturing 43
o More complex approach to managing both plant and enterprise data o Data management approach often must employ specialized data scientists o Data scientists are not intimately familiar with the process like engineers and operators o Which limits their ability to achieve the best results
Conclusion : Data – Driven Smart Manufacturing
Data – Driven Culture o Epistemic Curiosity o Data Accessibility o Data Automation
• Reduce : Report, Human ( manual ) error, Conflict • Unity (Dashboard), Real time
o Data is mentioned before decisions are made
44
Conclusion : Digital Transformation 45
“Digitalization” is a keyword in the next generation automation system Cyber-Physical Systems(CPS) and Internet of Things (IoT) are key
technological innovation. Every “Things” will be Monitored and Tracked through its product
lifecycle From the board room to the field engineer, decisions will be based on
a solid fact base created by big data analytics and automated recommendation.
Data-driven decision-making agile corporations will be a winner in future Internet of Things environment that their assets physically able to change themselves based on the new data without human intervention.
Conclusion : Automation Architecture 46
Science Math Arts Religion Technology
Automation Engineering
(Reliability , Economic , Safety , Performance)
Question ? : Choice of the Day 47
