22
IIoT Practical Implementation 9/2/2016 Jim Kosmala Vice President Engineering, Okuma Andy Henderson, PhD Industry Analyst, GE Digital

IIoT – Practical Implementation · IIoT –Practical Implementation 9/2/2016 Jim Kosmala – Vice President Engineering, Okuma ... OSP Control MTConnect Non-OSP Control

Embed Size (px)

Citation preview

IIoT – Practical Implementation9/2/2016

Jim Kosmala – Vice President Engineering, Okuma

Andy Henderson, PhD – Industry Analyst, GE Digital

Industrial Internet of Things

• Why Get Connected

• 3 Reasons some are not connected yet

• Applications in Production

Why Get Connected ?

MachineIntelligent

TechnologyMT Connect

GE Brilliant Factory

= Opportunity

5%

Connected

Lack of Machine

Knowledge

Avg. 65% Asset

Utilization - time

producing parts

Inefficiency +

Process Optimization/ Asset Utilization

Dashboards with real time production information

1. Get Connected

2. Get Insights

3. Get Optimized

Machine Health/Predictive-Preventative Maintenance

www.myokuma.com

Apps to Get Connected

Mobile Monitoring

Mobile Monitoring

Reasons Not Connected Yet:

Financial

1. 2.

3.

Scalable to Fit Your Needs

Software

Hardware

Data Communication

Machine Health

Predictive / Preventative Maint.

Quality, SPC

Energy Management

Communication

Scalable

Small

Medium

Large

Video Communication

MTConnect

OSP Control

MTConnect

Non-OSP Control

Predix - Embedded Predix - Ready

New Collaboration

Predix Machine

(Run-time app)

Predix Machine

(Run-time app)

Predix Cloud

Predix Client

Apps

Predix Field

Agent box

PC Data Server

How GE is applying

IIoT to Manufacturing

Manufacturing at GE

POWER

~$30B

66K EMPLOYEES

OIL & GAS

$18.7B

46K EMPLOYEES

AVIATION

$24B

45K EMPLOYEES

ENERGY

MANAGEMENT

~$11B

47K EMPLOYEES

RENEWABLE

ENERGY

~$9B

13K EMPLOYEES

APPLIANCES

& LIGHTING

$8.4B

25K EMPLOYEES

TRANSPORTATION

$5.7B

13K EMPLOYEES

HEALTHCARE

$18.3B

55K EMPLOYEES

2014 REVENUES

• $50B Manufacturing Spend

• 400+ Plants

• 1% Efficiency > $500M Cost Savings

Manufacturing improvements are very

important to GE!

Industrial Businesses:

Data-driven OperationsPhysics Meets Analytics and why IoT is not IIoT

• 1.5M square feet of Manufacturing Space

• Machining, Welding, Assembly, Spray Coating

• New make & Repair

• Components and Full Assembly

Greenville: Gas Turbines

16

7HAPower Output:

7HA.01 = 275 MW

7HA.02 = 337 MW

Power Output: 198 MW

7F.04

7E.03Power Output: 91 MW

9F.03

Power Output: 265 MW

Power Output: 231 MW

7F.05

ElectricProcess GassesBldg Mgmt

Compressed Air

Durable GoodsTool Life Mgmt

Process DataUtilization

PM TimersShop Floor Dashboard

Predictive

Consumable

Management

Energy

ManagementMachine

Health

Process

Optimization

Greenville: Gas Turbines

• 1000hp motor for #2

main air compressor

• Stopped before major

catastrophic failure

-------------------------------------------------------------------------------------------------------------

Cost Avoidance:

Prevented catastrophic failure

Eliminated need for new motor

Saved ~$###k in overhaul costs

Big Data Info Action $$$

Brilliant Factory Win – Machine Health

-------------------------------------------------------------------------------------------------------------

Cost Avoidance:

• Prevented unplanned system

downtime

• Avoided $##K/year in wasted gas

• High Argon flow during plant

shutdown led to “Treasure Hunt”

• Leak found at Coatings Furnace

• Leak totaled approximately 200cfh

Before find it, fix it

After find it, fix it

Big Data Info Action $$$

Brilliant Factory Win – Energy Management

Big Data Info Action $$$

-------------------------------------------------------------------------------------------------------------

Brilliant Factory Tools

Shift-By-Shift Utilization

& Completed Operations

Live Feed of

Machine Activity

Defect & Work-

Order Tracker

Machine Utilization – July 36.04%

Machine Utilization -

November50.9%

Cost Avoidance over 12

months$##k

Machin

e U

tiliz

ation %

Problem:

• Mill: Low utilization

• High demand for part

Issues Identified:

• Machine not completing

full cycles

• Defects and re-cuts

• Machine idle time

between parts

Improvements Made:

Improved cutting tool

utilization

Fixture and part loading

enhancements

Standard work schedule

for operators

Brilliant Factory Win – Process Optimization

More Sensor Analysis

Unsupervised Learning / ClusteringAutomatic Identification of Anomalies

Machine Learning (Spindle Load - Okuma MB 5000H)

1. Thermal Sensors Feedback To CNC

2. Vibration Sensors

Feedback to CNC

4. CNC Compensates

Axes Independently

3. CNC Calculates Thermal Error

Estimate thermal

deformation

Temperature &

Operation data

Thermal deformation

compensation… and other sensor data

IIoT – Practical Implementation

Thank You!