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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 analyticsModerator: Lucian Fogoros, IIoT WorldSpeaker: Serg Posadas, Clockwork Solutions
Ask a question!Use the chat tool or
tweet using #iiotvirtualconf
3
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
5
gathering data+ predictive modeling+ actionable insights= Strategic Value
Transforming data into timely insights and relevant actions
66
“million dollar” questions
• How can I improve total effective equipment performance?
• How do I get the most value out 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?
8
…buthow 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 operation changes
• can’t anticipate dynamic environment, aging, etc.
Stra
tegi
c va
lue
? •
What just happened?What will happen?
What should we do?Most solution providers
Next gen predictive analytics
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Traditional forecastinganalytics
• 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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Predictiveanalytics
• 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, obsolescence, etc.
• Provides a holistic view of complex scenarios
Predictive analytics provides a higher degree of accuracy
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Predictive analytics is determiningindustry 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 &
operations data
• Need automation
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CBMchallenge
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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1. Separate data into training and test sets.
2. Describe the data attributes in the training data set
Normal Pre-Failure FailureNormal NormalNormal
MachineLearning& CBM Training
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Normal Pre-Failure Normal Pre-Failure Failure
MachineLearning& 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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MachineLearning& 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.
NormalPre-Failure Pre-Failure Failure Normal Normal
New Predictions
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CBMreality
Sensor data is noisy & inconclusive
sensor 1
sensor 2
sensor 3
sensor 4
sensor 5
not so fast…
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Complete predictive view
Sufficient data set
0
2
4
6
8
10
0 100 200 300 400
Minor maintenance
actionsOperational profile
change Location of
operations
Operating Hours
Operating Hours
CI Value
Sufficient data set Time series behavior of each CI
Ideal data setAdd 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
Se
nsitiv
ity
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
0.00 0.20 0.40 0.60 0.80 1.00
1-Specificity
a
b
class
0.7209
0.7209
Area
Receiver Operating Characteristic
OBE
Se
nsitiv
ity
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
0.00 0.20 0.40 0.60 0.80 1.00
1-Specificity
a
b
class
0.5602
0.5608
Area
Receiver Operating Characteristic
Se
nsitiv
ity
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
0.00 0.20 0.40 0.60 0.80 1.00
1-Specificity
a
b
class
0.5218
0.5217
Area
Receiver Operating Characteristic
MDR IDA3
Se
nsitiv
ity
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
0.00 0.20 0.40 0.60 0.80 1.00
1-Specificity
a
b
class
0.5747
0.5747
Area
Receiver Operating Characteristic
ODA1
Se
nsitiv
ity
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
0.00 0.20 0.40 0.60 0.80 1.00
1-Specificity
a
b
class
0.5094
0.5099
Area
Receiver Operating Characteristic
OFM0
Se
nsitiv
ity
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
0.00 0.20 0.40 0.60 0.80 1.00
1-Specificity
a
b
class
0.6650
0.6649
Area
Receiver Operating Characteristic
IFM0
Individual CI’s
COMBINED SUPER CI
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• Evaluate many prognostic algorithms to determine best fit
COMBINED SUPER CI
0
0.2
0.4
0.6
0.8
1
1.2
1.4
0100200300400500
CI V
alu
e
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
26
AbatingFalse 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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CBMresults
• 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 & LCMbenefits
• 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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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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Turning data into 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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PlatformStudio Backbone
Life Cycle Management
Sensor Prognostics Design
Data Acquisition
Database access
Flat file
Big Data
Streaming Data
Data Quality & Transformation
ProductizedModels
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
3737
Studio: data mgt and analytics
Supports data scientists and analysts with powerful tools for managing, analyzing and visualizing data
3838
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
39
Global provider of high volume packing equipment
for shipping
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up
Up next 12:30pm (US Eastern) 11:30am (US Central) 10:30am (US Mountain)
9:30am (US Pacific)
Panel discussionHost: Lucian Fogoros, IIoT WorldPanel: Benson Chan, Strategy of Things
Aaron Allsbrook, ClearBladeSerg Posadas, Clockwork Solutions
Ask a question!Use the chat tool or
tweet using #iiotvirtualconf
42
Ask a question!Use the chat tool or
tweet using #iiotvirtualconf
[email protected] [email protected] [email protected] [email protected]