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

8

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

GB-M\Steve Sands

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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GB-M\Steve Sands

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