0Copyright © 2014 World Wide Technology, Inc. All rights reserved.

IoT in MiningBrian Vaughan - WWT

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Big Data IoT Approach

Big Data projects operate at the intersection of business, science, and technology

TECHNOLOGY• Captures and stores

data on business• Facilitates the

operation of data science

BUSINESS• Highlights areas of high

opportunity• Drives focus on value

creation

DATA SCIENCE• Solves business problems• Proves solutions based on empirical evidence

𝑓 𝑥 = 𝑎0 +

𝑛=1

∞

𝑎𝑛 cos𝑛𝜋𝑥

𝐿+ 𝑏𝑛 sin

𝑛𝜋𝑥

𝐿

$$$

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Defining The Opportunity Is The Starting PointThe power of “Big Data” lies in bringing together data in a timely fashion from sources within and external to the enterprise - structured and unstructured - to create a complete view of critical issues, therefore enabling advanced analytics to unlock key insights that drive significant value

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Outcome

Analytics

Data

Technology

Clearly defined use cases with the potential to deliver significant value by distilling vast data into new, previously unknowable intelligence

Advanced machine learning techniques to analyze data and mine for insights to drive critical decisions

Structured or unstructured, internal or external, requiring new methods of storage/integration

Emerging/new technology stacks using scalable, distributed architectures

33

FTP over

MESH

Data Logger

• One per truck

• (Logs, Sensors, OEM

Alarms, VIMS Service

Port)

Equipment Maintenance

Dispatch & Operator

Fuel, Oil Analysis, etc.

1Urgent Component Problem

2 Critical Sensor Problem

Stratify Alarms

3Important/Not Urgent Component/Sensor Problem

4 Not Important Component or Sensor Problem

5 Noise - Ignore

Data Driven Preventative Maintenance - Oil Changes

Data/Analytics driven timing for preventative maintenance (e.g., oil changes) on individual Trucks1 Urgent Component

Problems

e.g., Engine, Transmission, Differentials, Torque Converters, Final Drives

Predict Major Component Failure - Engines

Project Scope• 252 Trucks – 200

sensors per truck• 7 Mine sites• 10,000

readings/second

Data Integration• Integrating siloed data sources in multiple formats• 10 Terabytes of data• 3 year historical data ecosystem

Business Impact: Higher equipment up-time and reduced critical component failure

Using IoT for Predictive Maintenance

A set of predictive models drawing on all available data provided a leading mining company with an early warning system for surface mining equipment, allowing for more proactive preventative maintenance

C a s e S t u d yM i n i n g

M a i n t e n a n c e

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Engine Failure Model – How it works

y = b0 +b1x1 +...bnxn

60 day look-back

Examine a variety of data sources looking for patterns of bad behavior in the last 60 days

30 day forward prediction

88% probability of engine failure in the next 30 daysMost likely reason = engine oil differential pressure alarm

13% probability of engine failure in the next 30 days

Model Monitoring Layer

Model Experimentation Layer

Keep track of model performance over time, determine model refresh rate, and how changes in operations effect model performance

Experimenting with new algorithms and variable creation, and finding new insights based on the model’s output so far

Engine Dashboard

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• Decreasing the time it takes to transport copper ore from the shovels to dumps could save tens of millions of dollars per year

• Available data sources:− Sensor data− Gear speed tables− Call point timing*− GPS location, elevation*− Weather data*− Dispatch routes*

• Aspects of the cycle that can be influenced:1. Road maintenance2. Operator practices3. Truck maintenance

*denotes new data source

Haul Cycle ImprovementSeveral use cases have been developed to increase the efficiency of Freeport’s haul truck cycle; a variety of analytical techniques have been utilized to gain a better understanding of current practices and future opportunities

• Suspension cylinder pressures can be monitored to identify bumps in the road

• Sensor analysis has been confirmed by observations

• Trucks can communicate road conditions back to field teams

• New geospatial data from GPS sensor and virtual beacons allows view into grades of slope

• Combining road topology with transmission sensors shows when operators are in the incorrect gear

• Big Data ecosystem allows ad hoc queries of a large datasets

• ‘Sick’ trucks can be identified as slow compared with targets

• Trucks can be analyzed in a variety of conditions – wet/dry, day/night, loaded/empty, etc.

Goal: Increase Haul Cycle Efficiency

Enhance Road

Maintenance

Improve Operator Practices

Heal ‘Sick’ Trucks

grade

gear

1

2

3

Tru

ck ID

Secs over Target

Dry Rainy

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Haul Cycle Delays – Road Traffic Map

Key

• Under Target

• Near Target

• Over Target

• Call-point

Time Lapse

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Haul Cycle Delays – Road Quality Map

Key

• ‘Good’ Road Quality

• ‘Bad’ Road Quality

• Call-point

Time Lapse

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IoT Benefits to Mining

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Increased engine life through better preventative maintenance

Improved productivity from higher fleet availability

Faster cycle time from improved road quality

Reduced frame damage from improved road quality

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Thank YouQ&A

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Brian VaughanWorld Wide Technology

brian.vaughan@wwt.com