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Up next 11:40am (US Eastern) 10:40am (US Central) 9:40am (US Mountain) 8:40am (US Pacific) Transform your data into strategic business value with predictive analytics

Moderator: Lucian Fogoros, IIoT World Speaker: Serg Posadas, Clockwork Solutions

Ask a question! Use the chat tool or tweet using #iiotvirtualconf

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You’ve never had more data on your strategic assets

• historical data on operations,

maintenance, and inspections

• real-time and sensor data
• digital and virtual asset models

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But data ALONE provides NO strategic VALUE

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gathering data + predictive modeling + actionable insights = Strategic Value

Transforming data into timely insights and relevant actions

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“million dollar” questions

• How can I improve total effective equipment

performance?

• How do I get the most valueout of my assets?
• How do I avoid costs when operating and

maintaining my assets?

• How do I best manage spare parts to keep my assets

running?

• How do I best design my operation to employ my

assets?

• How do I maintain optimal uptime and asset-

generated revenue?

…So you can answer these

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Rear- facing BI is not accurate

Historical business “intelligence” is looking back at historical data in attempt to react.

What Just Happened?

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…but how do you get the right answers?

traditional analytics predictive analytics

• rear facing
• helpful after

the fact

• reactive
• forward looking
• provides warning and

actionable insight

• supports well-developed

strategies

• quantifies risk

traditional forecasting

• tied to historical data
• doesn’t account for
• peration changes
• can’t anticipate dynamic

environment, aging, etc.

Strategic value

?

• What just happened?

What will happen? What should we do? Most solution providers Next gen predictive analytics

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Traditional forecasting analytics

• Limited to historical view
• Can’t include future events,

policy changes, evolution of business environment

• Does not measure risk
• Very likely to incur greater costs or

more down time

• Lacks ability to support strategic

planning Traditional forecasting trades accuracy for ease of implementation

?

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Predictive analytics

• Insights well beyond rear facing

analytics

• Historical data only defines

starting point

• Models future events for each

asset and its components

• Simulates operations hour by

hour, including failures, repairs, shipments, part buys, refurbishment, retirement,

• bsolescence, etc.
• Provides a holistic view of

complex scenarios Predictive analytics provides a higher degree of accuracy

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Predictive

analytics is

determining

industry leaders

Within the next five years, advanced implementation of Industry 4.0 will become a ‘qualifier to compete’ and is also likely to be seen by investors as a ‘qualifier for funding’.

Industry 4.0: Building the digital enterprise, PwC 2016 Global Industrial Survey, April 2016

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IIoT predictive

analytics

Challenges

Data

• Multiple sensors
• High volume & velocity
• Complex distribution of sources

Obstacles

• Simple data but requires advanced techniques
• Combine asset health monitoring with maintenance &
• perations data
• Need automation

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CBM challenge

Asset health monitoring for predictive maintenance analytics

Benefits

• Leverage advances in predictive health maintenance
• Reduced unplanned downtime
• Control costs

Challenges

• Data quality
• Data structures
• Volume & velocity of real-time and historical data sets
• Prediction accuracy
• False positives

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Data aggregation

• Automate data aggregation & transformation
• Leverage latest techniques for data conditioning
• Combine data silos sources for complete, accurate prognostics

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Machine Learning & CBM

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• 1. Separate data into training and test sets.
• 2. Describe the data attributes in the training data set

Normal Pre-Failure Failure Normal Normal Normal

Machine Learning & CBM

Training

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Normal Pre-Failure Normal Pre-Failure Failure

Machine Learning & CBM

• 1. Separate data into training and test sets.
• 2. Describe the data attributes in the training data set.
• 3. Apply a predictive technique
• 4. Evaluate predictor with test data and measure error.
• 5. If not satisfied, try another predictor.

Repeat while minimizing error.

Test Predictions

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Machine Learning & CBM

• 1. Separate data into training and test sets.
• 2. Describe the data attributes in the training data set.
• 3. Apply a predictive technique
• 4. Evaluate predictor with test data and measure error.
• 5. If not satisfied, try another predictor.

Repeat while minimizing error.

• 6. Select best prediction algorithm.

Predict based on new values.

• 7. May have to re-train as conditions evolve.

Normal Pre-Failure Pre-Failure Failure Normal Normal

New Predictions

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CBM reality

Sensor data is noisy & inconclusive

sensor 1 sensor 2 sensor 3 sensor 4 sensor 5

not so fast…

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CBM condition indicators

train test

False Positive Rate 10% Anomaly Detection Rate 65%

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Complete predictive view

Sufficient data set

2 4 6 8 10

100 200 300 400

• perations

Operating Hours Operating Hours CI Value

Sufficient data set

Time series behavior of each CI

Ideal data set

Add maintenance and operational events

• Maintenance Actions
• Operational Profile Changes
• Operation Locations

Goal: Develop accurate prognostic Requirement: Study Condition Indicator (CI) across lifetime of a component.

CI Value

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Super condition indicator

• Individual CIs often may not have sufficient prognostic power
• Leverage Super CI to increase predictive resolution

OBE

MDR IDA3

ODA1

OFM0

IFM0

Individual CI’s COMBINED SUPER CI

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Determine the best fit

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• Evaluate many prognostic algorithms to determine best fit

COMBINED SUPER CI

0.2 0.4 0.6 0.8 1 1.2 1.4

100 200 300 400 500

CI Value Hours to Removal

Degradation

Hours to Removal

200 175 150 125 100 75 50

LDA 35% 37% 57% 59% 71% 93% 100% Gaussian Naïve Bayes 39% 42% 46% 53% 68% 89% 100% K Neighbors 65% 57% 57% 58% 68% 82% 100% QDA 37% 39% 43% 51% 65% 84% 100% Linear Support Vectors 38% 38% 42% 47% 60% 77% 80% Non-linear Support Vectors 29% 30% 34% 43% 55% 77% 88% Logistic Regression 37% 37% 39% 43% 55% 70% 56% Stochastic Gradient Descent 21% 15% 24% 34% 35% 59% 4% Ridge Classifier 35% 34% 37% 43% 53% 73% 68%

Determine the best fit

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Anomaly detection

• Identify distinct probability distributions for Super CI indications
• Produce failure lead time

COMBINED SUPER CI Healthy Component Anomalous Component

Anomalous Component with >100 hours to causal removal

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Abating False positives

• Analysis select wait time required to distinguish

between false positive and true anomaly

• Reduces negative impact on maintenance and supply
• Account for spikes and dips in the data
• Manage data quality with cleansing and

transforation

• Determine the optimal time for

maintenance

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CBM results

• Provide repair lead time

– Reduce wait times for maintenance & parts – Optimize labor

• Avoid catastrophic failures
• Reduce logistics response time
• Control impact of failures on operations
• Extend asset life
• Minimize unplanned downtime

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But What About…

• Parts with no sensors
• Long term strategies
• Impact on costs
• Inventory Optimization
• Future Performance metrics

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But What About…

• Parts with no sensors
• Long term strategies
• Impact on costs
• Inventory Optimization
• Future Performance metrics

Asset Life Cycle Management

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Life Cycle

Management

(LCM)

Benefits

• Strategic approach to long-term asset planning
• Accurately managing future costs and expenses
• Maximizing uptime and revenue

Uses

• Managing components and assets with and without sensors
• Accounting for changes in operations, upgrades, …
• Anticipating dynamic conditions & evolving environment
• Evaluating alternate future scenarios

Asset Operations Maintenance Supply Logistics Sustainment

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CBM & LCM benefits

• Evaluate impact of full asset BOM
• Design maintenance strategies
• Integrate operational changes with

maintenance planning

• Optimize future enterprise inventory
• Control Costs
• Attain future business goals

– Maximize up time & readiness – Control risk

• Budget
• Operations

– Maximize Revenue

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About us

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Quick facts

• Unique focus on capital

intensive physical assets

• Experience across

multiple industries

• Software built out of

service focus

• Over 30 years of experience
• Based in Austin, Texas;

deployed worldwide Predictive health management Condition-based maintenance Life Cycle Management Performance based logistics Data management

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…and trusted by

• ur clients

(including but far from limited to…)

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Turning datainto information supporting decisions, processes and automation

Raw Data Aggregation Data Cleansing & Consolidation Predictive Model Design Operationalizing Predictive Output Automated Decision Making Optimization

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Platform

Studio Backbone

Life Cycle Management Sensor Prognostics Design Data Acquisition

Database access Flat file Big Data Streaming Data

Data Quality & Transformation

Productized Models

Analytics Libraries

Machine Learning Neural Networks NLP and more

Data Visualization

Viz Libraries Reports Dashboards

Discrete Event Simulation

Simulation Engine Asset models Metric Aggregation

Analytics Software Platform

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Studio:

data mgt and analytics

Supports data scientists and analysts with powerful tools for managing, analyzing and visualizing data

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LCM: modeling asset lifecycles

Represents

• Aging of assets
• Changes in operations
• Retirements &

Acquisitions

• Repair degradation
• Risk and Uncertainty

Includes

• Deep asset indentures
• Asset age & condition

initialized from raw data

• Detailed baseline and

alternative cases

• Simulation output data that

would otherwise not exist

• Future supply, sustainment

and maintenance changes

• Best performance at least cost

Asset Operations Maintenance Supply Logistics Sustainment

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Business challenges

• Equipment maintenance is an extensive expense
• Multiple process control sensors with potential to detect

impending issues before failure

• Long maintenance lead times lowering customer satisfaction

Solution

• Smoothing algorithm to the raw sensor data

and maintenance history data

• Baseline anomaly detection prognostic by

combining five sensor readings

• One month historical data to train algorithm
• Advance warning on impending failures

– 5 months for heater wire – 2 months gerotor

ROI

• Predictive Heath Maintenance (PHM) prognostics
• Advance scheduling of maintenance before issues arise

case study:

Industrial Packaging Machines

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Global provider of high volume packing equipment for shipping

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Questions

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Up next 12:30pm (US Eastern) 11:30am (US Central) 10:30am (US Mountain) 9:30am (US Pacific) Panel discussion

Host: Lucian Fogoros, IIoT World Panel: Benson Chan, Strategy of Things Aaron Allsbrook, ClearBlade Serg Posadas, Clockwork Solutions

Ask a question! Use the chat tool or tweet using #iiotvirtualconf

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Ask a question! Use the chat tool or tweet using #iiotvirtualconf

aallsbrook@clearblade.com sposadas@clockwork-solutions.com benson@strategyofthings.io lucian.fogoros@iiot-world.com