This document discusses predictive analytics for IoT devices. It begins by showing how simple univariate models can correlate vibration levels with equipment failure rates. However, more accurate multivariate and ensemble models are needed by incorporating additional input variables and advanced data preparation. The models can then forecast wear and fatigue damage to predict failure probabilities and estimated dates. Operationalizing involves feeding real-time sensor data into databases, running predictive analytics, and taking recommended actions such as maintenance scheduling. Building and regularly retraining accurate failure prediction models provides valuable insight for maintenance planning.

1. Predictive Analytics for IoT
Michael Adendorff
Architect, STSM IBM
IBM Predictive Maintenance and Quality
michael.adendorff@ca.ibm.com

3. Evidence , Clues > Failure Prediction

4. Predictive
Analytics
Valuable
Insight

5. Maintenance Insight

6. Maintenance Insight
Failure Risk: Under Maintained Equipment

7. Maintenance Insight
Wasted $$$$$: Over maintained equipment

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

10. Parts Requirements Forecast : Main Bearing
June: July: Aug:
10 3 22
9 20 12
7 21 14
12 15 17

11. Business Results : Predictive Maintenance
Downtime
Unplanned
Planned

12. Predictive
Analytics
Valuable
Insight
How does it work?

13. Simplistic Illustration
Historic Data
Failure
Records
Vibration
Levels
Correlation
FailureCount Vibration Level
More failures have been
witnessed when vibration
levels are high

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
Lorem ipsum dolor sit amet,
consectetur adipiscing elit. Ut
lacinia semper gravida. Morbi vel
orci in leo malesuada malesuada
in ac enim. Nam pulvinar nec
enim in venenatis. In nibh turpis,
sodales at fermentum in
Sensors don’t record every
causal factor. Text analytics is
used to fill in some of the blanks.
p(fail)
E(fail date)

26. Predictive
Analytics
Valuable
Insight
Building models is only half the fun. Next step – OPERATIONALIZE

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

29. Predictive Analytics done in realtime
Event
Master
Data
Profile
KPI
p(fail)
E(fail date)

30. Predictive Analytics done in realtime
Event
Master
Data
Profile
KPI
p(fail)
E(fail date)
Predictive Outputs fed back as
new events

31. Deciding on Recommended Actions
Event
Profile Action
KPI

32. Taking Action
REST DB
WebService
FTP Other

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

34. Questions?
Michael Adendorff
Architect, STSM IBM
IBM Predictive Maintenance and Quality
michael.adendorff@ca.ibm.com