IBM Predictive analytics IoT Presentation
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Transcript of IBM Predictive analytics IoT Presentation
![Page 1: IBM Predictive analytics IoT Presentation](https://reader030.fdocuments.net/reader030/viewer/2022020110/554f7294b4c905c8088b56c8/html5/thumbnails/1.jpg)
Predictive Analytics for IoT
Michael Adendorff Architect, STSM IBM
IBM Predictive Maintenance and Quality [email protected]
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Evidence , Clues > Failure Prediction
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Predictive Analytics
Valuable Insight
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Maintenance Insight
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Maintenance Insight
Failure Risk: Under Maintained Equipment
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Maintenance Insight
Wasted $$$$$: Over maintained equipment
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Work Order
Urgent Inspection Required: High probability of failure
High risk of failure before next scheduled maintenance
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Maintenance Schedule Update Request
Bring forward scheduled maintenance to Jul 5
Bring forward scheduled maintenance to Aug 7
Delay scheduled maintenance to Dec 15
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Parts Requirements Forecast : Main Bearing
June: July: Aug:
10 3 22
9 20 12
7 21 14
12 15 17
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Business Results : Predictive Maintenance
Downtime
Unplanned
Planned
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Predictive Analytics
Valuable Insight
How does it work?
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Simplistic Illustration
Historic Data
Failure Records
Vibration Levels
Correlation
Failu
re C
ou
nt
Vibration Level
More failures have been witnessed when vibration levels are high
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Univariate Model
Failu
re C
ou
nt
Vibration Level
Vibration Level
P Failure Confidence
< 0.1 0.1 % 2%
0.-0.5 1% 3%
0.5 – 2 3% 5%
2 – 5 15% 10%
+5 98% 80%
p(fail)
Simple univariate models are generally not very accurate. This one looks better than it is. High vibration strongly correlated with failure as it is a lagging indicator. Need leading indicators to predict.
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Multivariate model
p(fail)
More accurate than the univariate model, but raw input data never reveals the whole story.
Correlates failures with combinations between multiple input variables
Historic Data
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Advanced Data Prep + Ensemble Models
More accurate than the univariate model, but raw input data never reveals the whole story.
Historic Data
p(fail)
E(fail date)
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Advanced Data Prep + Ensemble Models
More accurate than the univariate model, but raw input data never reveals the whole story.
Historic Data
Cumulative Cycles = f(speed, operating hours)
p(fail)
E(fail date)
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Advanced Data Prep + Ensemble Models
More accurate than the univariate model, but raw input data never reveals the whole story.
Historic Data
Cumulative Fatigue Load = f(Cycles, Speed)
p(fail)
E(fail date)
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Advanced Data Prep + Ensemble Models
More accurate than the univariate model, but raw input data never reveals the whole story.
Historic Data
Wear Damage Forecast
p(fail)
E(fail date)
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Advanced Data Prep + Ensemble Models
p(fail)
More accurate than the univariate model, but raw input data never reveals the whole story.
Historic Data
Wear Damage Forecast
E(fail date)
Wear Modeling
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Advanced Data Prep + Ensemble Models
More accurate than the univariate model, but raw input data never reveals the whole story.
Historic Data
Fatigue Damage Forecast
p(fail)
E(fail date)
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Advanced Data Prep + Ensemble Models
p(fail)
More accurate than the univariate model, but raw input data never reveals the whole story.
Historic Data
Wear Damage Forecast
E(fail date)
Fatigue Modeling
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Advanced Data Prep + Ensemble Models
Building models like this requires brute force number crunching as well as skills and knowledge. Payoff comes from more accurate predictions – but – it doesn’t end here.
Historic Data
Time series forecast + Combination Model
p(fail)
E(fail date)
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Advanced Data Prep + Ensemble Models
Historic Data
Expected failure date is more actionable than current probability of failure
Building models like this requires brute force number crunching as well as skills and knowledge. Payoff comes from more accurate predictions – but – it doesn’t end here.
p(fail)
E(fail date)
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Advanced Data Prep + Ensemble Models
Historic Data
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Sensors don’t record every causal factor. Text analytics is used to fill in some of the blanks.
p(fail)
E(fail date)
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Predictive Analytics
Valuable Insight
Building models is only half the fun. Next step – OPERATIONALIZE
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Feed Data
APIs for: • Describing target data structures • Describing calculations and aggregations • Running analytics • Exposing analytic results
REST Historian DB
Web
Se
rvic
e
MQTT Other
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Data flows into DB in realtime
Event
Master Data
Profile
KPI
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Predictive Analytics done in realtime
Event
Master Data
Profile
KPI
p(fail)
E(fail date)
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Predictive Analytics done in realtime
Event
Master Data
Profile
KPI
p(fail)
E(fail date)
Predictive Outputs fed back as new events
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Deciding on Recommended Actions
Event
Profile Action
KPI
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Taking Action
REST DB
Web
Ser
vice
FTP Other
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Valuable Insight
Build Models 1) Assemble historic data 2) Attempt to correlate historical data with a
known target 3) Improve results by putting more thought
about preparing inputs and algorithm selection
Operationalize 1) Feed raw data 2) Describe calculation and aggregation 3) Perform analytics 4) Carry out decision logic 5) Feed results 6) Retrain models regularly
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Questions?
Michael Adendorff Architect, STSM IBM
IBM Predictive Maintenance and Quality [email protected]