8. Work Order
Urgent Inspection Required: High probability of failure
High risk of failure before next scheduled maintenance
9. Maintenance Schedule Update Request
Bring forward scheduled maintenance to Jul 5
Bring forward scheduled maintenance to Aug 7
Delay scheduled maintenance to Dec 15
14. Univariate Model
FailureCount
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.
15. 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
16. 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)
17. 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)
18. 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)
19. 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)
20. 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
21. 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)
22. 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
23. 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)
24. 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)
25. 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)
27. Feed Data
APIs for:
• Describing target data structures
• Describing calculations and aggregations
• Running analytics
• Exposing analytic results
REST Historian DB
WebService
MQTT Other
28. Data flows into DB in realtime
Event
Master
Data
Profile
KPI
33. 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
