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

Unrestricted © Siemens 2019

Unrestricted © Siemens 2019 All rights reserved. Community. Collaboration. Innovation.

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Unrestricted © Siemens AG 2019 Smart Factory Summit 2019

The language of today

what can data do?

digital transformation

predictive maintenance

data analytics

AI

machine learning

big data


Can data redefine entire industries?

vs

HUMAN MACHINE

Exploiting

Inefficiencies


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


And the results are…

$124,343,900 $242,065,828VS

#18/30 #1/30


Where else is data making an impact?


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


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


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


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


5.5 million new “things” get connected

every day, and 50 billion by 2020

A lotcan happen

Source: Gartner Research

in a day


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


Can you show me how this applies to

manufacturing?


The matchup in Manufacturing

vs

HUMAN MACHINE

Exploiting

Inefficiencies

Manufacturing

Operations

© Siemens AG 2017

April 2017 siemens.com

The field–

Sources of digital information

© Siemens AG 2017


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

© Siemens AG 2017


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

© Siemens AG 2017


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.

© Siemens AG 2017


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.


Can you discuss some other examples?

© Siemens AG 2017


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

© Siemens AG 2017


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

© Siemens AG 2017


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


It seems a bit overwhelming. How do I get

started?

© Siemens AG 2017


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

© Siemens AG 2017


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Seminar Slides

After MiA, seminar slides will be available for download at:

http://www.attendmia.com/download/seminars

http://www.attendmia.com/download/seminars

Unrestricted © Siemens 2019 All rights reserved. Community. Collaboration. Innovation.

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]

Getting Started with MindSphere and IIoT · MindSphere Fleet Manager Value Optimized Tool Life...

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