Post on 14-Jul-2018
transcript
GB-M\Steve Sands 1
Steve Sands
Festo GB
Maintec 2017
How the fourth Industrial Revolution will usher
in a new approach to maintenance activities
• Festo journey as a supplier and user
• Outlook for Smart Maintenance
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Display/sensing
Controlling
Driving
Gripping
Connectivity for now and the future
Bildplatzhalter?
Controllers
Electric drives
Operator units
Mechanical
Valve terminals Valves
Vacuum
PneumaticServo pneumatic
Sensors Compressed air
preparation
Connection
technology
Festo: global control and automation manufacturer
Components, assemblies and systems for factory and process automation
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Internet of Things Industry 4.0
... digitising production,
bringing together the real and virtual
worlds - cyber and physical…
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Automation:
Speed Efficiency Cost OEE
Product variety Flexibility
Consistency Traceability
Validation Reproducibility
Modularity Integration
Serviceability Diagnostics
Automation enablers:
Standardised communications
Improved interfaces [H2M, M2M]
Improved sensor technology
Cloud computing developments
Wireless communications
Data visualisation and analysis
People + Automation
engaging
embracing
enhancingNew technology: new skills, knowledge
Culture change: new company structures and networks [depts., teams, suppliers & customers]
and enablers trends
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German government: Platform Industrie 4.0
5
Creation of the term ‘integrated Industries‘ or Industrie 4.0 ~ 2010
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Industry 4.0: complete user digitalisation strategy
Energy
Efficiency /
Transparency
Lean
AssemblyBig Data Human-
Machine
Interfaces
(HMI)
Manufacturing Execution
System (MES)
Open-Protocol-
Communication Unified
Architecture (OPC-UA)
Real-Time
communication
Near Field
Communication
(NFC) RFID
Intelligent
Logistics
Radio-Frequency
Identification
(RFID)
Enterprise-Resource-Planning
(ERP)
Autonomous
Guided Vehicles
(AGV)
Cyber Physical
Production
Systems
(CPS/CPPS)
Augmented
Reality
Soft / Collaborative
Robotics
Adaptive
Structures
Decentralised
control
Condition
Monitoring
Autonomous
Behaviour
Cloud
Apps
Reconfigurable
automation /
manufacturing
Digital
maintenance
Virtual /
digital twin EmulationSimulation
3-D
printing
Wireless(IO Link / Zigbee
/ Bluetooth)
Qualification &
Management 4.0
Manufacturing Execution (ME)
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Lead factory for valves, valve terminals and electronics
Applying Industry 4.0: new Scharnhausen plant
Automated and
flexible plant
Flexible,
flowing production
Optimised energy
consumptionLearning factory
embedded
Highly flexible, energy-
efficient assembly lines
Optimised information
and materials flow
Energy network for buildings
and production processes
Training factory integrated
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Industry 4.0: a roadmap of standards
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1. Standardised functionality
2. Description template
3. System architectures
4. Relationship between automation technology and IT
5. Specification of modelling languages
6. Addressing non-functional properties
7. Modelling languages
8. Safety, Security and IT Information security
9. Line based communication
10. Wireless / radio based communication
11. EMC
12. Additive manufacturing
13. Human Beings (TOP = Technology, Organisation, Personnel)
14. Open source development
15. Transparent and seamless database and development tools
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Role of standards for open communication
Structured assets in the digital factory
e.g. product keys
RAMI [Reference Architecture Model for Industry]
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Industry 4.0 - common language(s)
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Standards for software architecture and machine language throughout the value creation chain
Horizontal and vertical integration
/ AS-Interface / Zigbee
Communication at the shop floor level
OPC UA
Interface standard making data
available across systems
<Automation ML/>
Embedding data
ServiceMaintenanceCommissioningProductionEngineeringConfiguration
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• Secure, protected, accessible
• Platform and vendor independent
• Standardised connectivity via internet
• Service Oriented Architecture
OPC - Unified Architecture
CODESYS Softmotion controller
Comprehensive information models
Securely exchanging structured data
“...who am I ...”
“...what can do I ...”
“...what am doing I ...”
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Automation Markup Language
Attributes
Devices
Networks
Mechatronics
Plant topology
Geometry
Kinematics
Manufacturer
Versions …
Embedded, structured information
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Digital product model
Digital twin
Saved (embedded) data
• manuals
• critical indicators
• failure modes
Standardised, structured data
Machine readable representation of production systems
Whole product lifecycle model: simulation and emulation
Current data
• monitoring performance
• wear
• environment
• trends
Modelling
Insights
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Industry 4.0 – improved control, organisation, efficiency…(?)
- will only be implemented where a clear customer / business benefit can be identified
1
5
Implementation of Industry 4.0 is an evolutionary process
it will progress at different speeds, in different industry segments
Action area Potential customer benefits
Production Costs, flexibility, adaptive production, ‘plug & produce’, batch size 1
Energy Management Increasing resource efficiency – matching process and output demands
Predictive Maintenance Increasing machine availability, condition monitoring, smart maintenance
Engineering Processes Faster commissioning of machines - ‘virtual commissioning’
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Industry 4.0: vision and innovation for tomorrow’s production
+ + +
=
Value added services
• Analysis
• Condition monitoring
• Cloud services
Business models
• Pay per user
• Predictive maintenance
• Customer support
Product NetworkingIntelligence Communication
Industry 4.0
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New business models: an interconnecting network of ‘things’
Energy efficiency: car optimally braking, regulating its speed
Adaption: car chooses an alternative route
Networking: All traffic lights co-ordinated according to the traffic situation, pollution
Business model: how much would YOU pay for your personal 'green wave'?
…
+ Car
+ Sat nav
+ Network
no accidents
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New business models: an interconnecting network of ‘things’
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+ Car
+ Sat nav
+ Network
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Energy monitoring & diagnosis for Smart Maintenance
Transparent energy management from component to factory network
Self-management for consumers
Active energy management
Smart Grid compatible
Detailed data analysis -
benchmark and comparison
1 Measure Real-time visualisation
• all energy consumers
• all applications
3 Manage
2 Control
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New levels of human-machine cooperation
Example: work organisation in the digital industrial era
Direct collaboration between people,
machines and robots
Intuitive and simple operation of
machines and systems
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Digital maintenance with App developed in-house
Error logsStatus
Operating
performance
Energy
usage
Condition
Monitoring
Fault finding
guidance
Manuals
Replacement
parts stock /
orderingMobile ‘depth’ sensor
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Maintenance in the past - complex paths and processes
Situation (Best Case)
Weaknesses
Machine
Maintenance engineer works from
maintenance dept. office.
Receives notification of breakdowns
via desktop PC.Way
Way
Way
Attends machine and makes
diagnosis
Goes to stores for replacement parts
Returns to machine to complete
maintenance repair
Potential• Limited information
• Multi locations, long distance walks
• Reporting, feedback, status
at maintenance office, no work-flow
• Avoid distances (save time+)
• Increase availablilty (reliability on delivery+)
• Have stable and reliable processes
Maintenance office
Stock
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Innovative solution: Digital Maintenance
New workflow
Optimisations• Direct path M-M (guided)
• Documentation on tablet
• Real time information
• Time & travel savings
Maintenance work directly from one
machine to the next
Diagnosis and fault analysis guided
Collects verified parts from stock.
completes repair
Great time saving
Maintenance notified of breakdown on
tabletsWay
Way
Way
Maintenance office
Machine Stock
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Smart Maintenance for Smart factories
Maintenance is a critical role
• More machines to be maintained
• Wider variety of technologies
Maintenance can not and will not, be replaced
• Creativity and flexibility in identifying and solving problems
• Shift from corrective to condition based predictive maintenance
Inspecting Servicing Repairing
Enhancing Monitoring Planning
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Smart Maintenance for Smart factories
Systematic training
QualificationElectronics
Mechatronics
Condition monitoring
System engineering
Information Science
Analytics
Future needs
• Understanding complex technology relationships
• Undertaking more remote monitoring and assistance
• Supporting machine generated data and risk analysis
• Understanding self controlling and optimising machinery
• Enhancing AI data
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Smart Maintenance – data driven
Remote
assistance
Self-optimisation
Enhancement
Predictive data
analysis
Valu
e
Data
Str
uctu
red
info
rmation
Analy
sis
Insig
ht
Condition
monitoring
Role of data is changing
1. Result of processes
2. Enabler of processes
3. Enabler of products
4. Product
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Sensor developments
S3 [Smart Sensor Systems]Microcontrollers, sensors, thin displays and integrated
communication interfaces embedded in thin film, flexible
substrate
Harvesting energy: magnetic field, induction coils
Wireless communication: distance, bandwidth, energy
Sensorfusion: stored reference data
Valves and valve manifolds
Integrated detection in valves:
• spool movement
• pressure change
• current drain
Actuators
Condition monitoring
Predictive maintenance
…
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Intelligent data exchange and analysis
Storage
Analysis
Processing
Condition
monitoring Continuous data
exchange
between components
and the cloud
Predictive
maintenance
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Data access and regulatory frameworks
Private Shared Public
Data generators
Data owners
Data users
Example: Car assembler
Line builder
Component manufacturers
MRO provider
or new ‘service’ provider
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Further development in
harmonised communications
Future success factors
Open Common
Standards
Further development
of open standardsRegulatory framework for the
ownership and sharing of data
Education and engagement of people
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Teaching and research platform for Industry 4.0
C-P Factory
Examples:
RFID modules for task management
Supply monitoring via CP systems
Service / Asset management via
augmented reality applications
3
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Bionic Concepts: demonstrating technologies for the future
Adaptation Function integration
Collective Behaviour
Human-machine
interaction
Collaboration
Energy efficiency Self-organisation Self-learning
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Festo automation Bionic Learning
NetworkFesto Didactic
Training/
consulting
Learning
systems
Inspiration from Nature
Human-Machine-Interface
Operations in action
Interaction and organisation
People & competences
Process
automation
Factory
automation
Customer-specific solutions
Factory and process automation combined with training
Festo Industry 4.0 portfolio at a glance
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Further information
Two day Industry 4.0
training workshop
• Technology
• Impact assessmentWhite Paper
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Thank you
Please come and see
us on Stand M375
Steve Sands
Product Management
Festo GB
Maintec I4.0