Getting Started with MindSphere and IIoT · MindSphere Fleet Manager Value Optimized Tool Life...
Transcript of Getting Started with MindSphere and IIoT · MindSphere Fleet Manager Value Optimized Tool Life...
Getting Started with MindSphere and IIoTPresented by John Auld and Greg Terhune
Manufacturing in America │ March 20-21, 2019
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Before we start… A Penny for Your Thoughts
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The language of today
what can data do?
digital transformation
predictive maintenance
data analytics
AI
machine learning
big data
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Can data redefine entire industries?
vs
HUMAN MACHINE
Exploiting
Inefficiencies
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Can you really predict the future with data?
2014
Sports Illustrated Predicted 2017 World Series Winners
2014 Team Numbers
$44M vs $235M
30/30 – Lowest payroll
2nd to last in their division
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And the results are…
$124,343,900 $242,065,828VS
#18/30 #1/30
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to smart home
From
manual control
to efficient fleet operations
From
trucksto powerby the hour
From
turbines
to streaming
From
record store
Digital business models – On the way
to a user centric mindset generating new values
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to streaming
to powerby the hour
to smart home
to efficient fleet operations
Digital business models – On the way
to a user centric mindset generating new values
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The new economy is customer centric platforms with a managed
eco-system of applications
… something is happening …
The world’s largest taxi company,
owns no vehicles
The world’s most popular media
owner, creates no content
The most valuable retailer,
has no inventory
The world’s largest
accommodation provider,
owns no real estate
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90% of the data in the world today has
been created in the last two years
Source: IBM, “10 Key Marketing Trends For 2017,”
A lotcan happenin a year
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5.5 million new “things” get connected
every day, and 50 billion by 2020
A lotcan happen
Source: Gartner Research
in a day
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Digitalization Changes Everything, Everywhere
The pace of technological advances is fueling digital transformation
DRONES
3D
PRINTING
INDUSTRIAL
ROBOTS
SENSORS
SMART
PHONES
COST PER UNIT
2018
2018
$40,000
$100
2007
2018
2007
2007
2007
2018
2007
2018
The cost of key technologies is falling
Cost of technology200
100
10
1
0
$ PER 1 MILLION TRANSISTORS
Source: Accenture Technology Vision 2015
Transistor density
’92 ’93 ’94 ’95 ’96 ’97 ’98 ’99 ’00 ’01 ’02 ’03 ’04 ’05 ’06 ’07 ’08 ’09 ’10 ’11 ’12 ‘13 ’14 ’15 ’16
Source: Leading Technology Research Vendor
$100,000
$200
$550,000
$20,000
$30,000
$50
$449
$10
100000
10000
1000
100
10
1000000
1000 TRANSISTOR COUNT
Implications of Moore’s Law
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Can you show me how this applies to
manufacturing?
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The matchup in Manufacturing
vs
HUMAN MACHINE
Exploiting
Inefficiencies
Manufacturing
Operations
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To
tall
y I
nte
gra
ted
Au
tom
ati
on
Enterprise Level
Management Level
Operator Level
Control Level
Field Level
SIMATIC NETIndustrial
Communication
SIMATIC IDENTIndustrial
IdentificationSIMATIC
Distributed I/OSINAMICS
Drive Systems
SIRIUSIndustrialControls
TIA PORTALEngineering
Framework for
Automation
Tasks
SIMATIC IT
Intelligence Suite
SIMATIC IT
Production Suite
SIMATIC IT
R&D Suite
ERP
SIMATIC WinCCSCADA System
PLM
SCADA
MES
TECNOMATIX
Digital
Manufacturing
TEAMCENTER
Collaborative
PDM
NX
Product
Development
Min
dS
ph
ere
PLM
MES
TIA
SIMATIC Controllers
SINUMERIKCNC
SIMATICHMI
SIMOTIONMotion Control
ISA 95
Level
4
ISA 95
Level
0
ISA 95
Level
1
ISA 95
Level
2
ISA 95
Level
3
The architecture–
Sources of digital information
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Example - Predictive Maintenance
Typical sources of vibration in a drive-train
MisalignmentUnbalance
Soft foot
Electrical,
Field faults
Roller bearing damages,
Rotating looseness
Gear meshing faults
Resonances
Vane faults
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Example - Predictive MaintenanceData exploration – Validating data and model creation
Problem: Strong Caustic Instability Data Model Validation Analysis I
Analysis III Analysis IIRoot Cause Identification
Those customers want to browse and explore the available data using statistical methods. They are looking for a tool that provides guidance to navigate
through data and apply the provided methods to investigate root causes of problems and identify business potential within the data.
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MindSphere
Art of the possible
Enabler: Connectivity to any brand of “Industrial Thing” (sensors, PLC’s, Meters, Enterprise Applications, etc…
MindSphere The cloud-based, open IoT operating system Platform as a Service
Process
Industries
Manufacturing MobilityPower
Generation
Buildings Wind Health-
care
Grid
Automation ERP MOM Energy Logistics
Enabler: Low / No code App development.
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Challenges
• Multiple OEM Platforms and Systems
• Motor Failures Leading to Unplanned
Downtime
• Optimization of Maintenance
Transformation Results
• Standards-based Integration to MindSphere
• X-Tools Edge Analytics Pre-process High
Frequency Vibration Data
• Comprehensive Rules for Failure Alarming in
MindSphere Fleet Manager
• Visual Data Analytics and Reporting
Value
• Predictive Analysis of Robot Failures
• Improvement of Quality and Efficiency
through KPI Monitoring
• Efficient Production Line Maintenance
Scheduling
• Increased Machine Uptime
• Standard platform for all OEM machinesFoto: KUKA
Digital Twin of the Performance – Industry Case
MindSphere – Robot Predictive Failure
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Challenges
• Costs and Time Required for EU Audit Process
• Determining Energy Consumption by
Process/Device
• Digitalizing Entire Energy Consumption
• Reduce Overall Energy Consumption Costs
Transformation Results
• Implemented PowerManager - Siemens Power
Monitoring System
• KPI Tracking Determining Energy Usage by
Activity
• Detailed Analysis of Largest Energy Consumers
Value
• Reduced Total Energy Consumption by 15%
• Improved Energy Cost Management
• Helped Define an All New Business Model
• Improved Customer Excellence Through Complete
Energy Use Transparency
Digital Twin of the Performance - MindSphere –
Energy Efficiency in Real Time
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Challenges Components Wear as Machine Time Increases
Components Must be Replaced Reaching Critical
Threshold to Avoid Loss of Quality, Productivity and
Higher Costs including Premature Replacements
Difficulty Detecting Defective Components
Increased Waste e.g., Re-Machining Parts
Transformation Results Industry Standard Integrations and Data Collections
into MindSphere Platform
Continuous, Automatic Data Collection and
Evaluation on MindSphere – Detailed Monitoring of
Feed Force, Machining Time, Tool Life, …
Comprehensive Rules for Failure Alarming in
MindSphere Fleet Manager
Value Optimized Tool Life Management
Strategically Scheduled Maintenance
Planning Safety and Cost Efficiencies
New Business Models and Services
Greater Process and Machine Performance Driving
Higher Quality and Services
Digital Twin of the Performance – Industry Case
MindSphere – Condition Management
MindSphere MindSphere
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It seems a bit overwhelming. How do I get
started?
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Industry experienced services support you to digitalization
Deep-dive workshop to identify use cases with
our proven MindSphere ideation approach1
Implementation of one of the identified package
use cases using planned, phased delivery2
Deployment planning
For higher transparency and savings at scale3
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How did we do? Don’t forget to leave your feedback in the app.
Got a minute? Rate this seminar via MiA App!
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Seminar Slides
After MiA, seminar slides will be available for download at:
http://www.attendmia.com/download/seminars
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Questions?
John Auld
Account Executive
Detroit
Phone: 313.300.5267
E-mail: [email protected]
Greg Terhune
Account Executive
Boston
Phone: 857.272.4435
E-mail: [email protected]