Predictive maintenance uses sensors and data analytics to predict failures in machines before they occur. It aims to replace parts that will break imminently, rather than all parts preventatively. This approach reduces costs compared to traditional preventative maintenance. Developing an effective predictive maintenance program requires understanding machine operations and economics, identifying relevant data sources, and creating predictive models. It is a collaborative process without single solutions, as each machine system presents unique challenges. The goal is finding the optimal balance of predictive efforts and resulting cost savings.

1. What is Predictive
Maintenance?

2. Production depends on machines,
and they need to work well.

3. When a
machine
breaks, this
costs time,
money,

4. …lives.

5. That is why we preventively maintain them.
Better to fix one hundred parts that could break than to have
one of them broken at a critical time.

6. In a perfect world,
we could predict
which part will break
and exactly when.

7. But in most cases prediction is only stochastic.
Out of 1000 items, we know about 10
will break.
However, we don’t know which ones.

8. Predictive (PM): replace the 10 lightbulbs that
will actually break.
But how to find those 10?
Preventive: replace all 1000 lightbulbs.
MAINTENANCE

9. Traditional
predictive
maintenance
using the most
versatile
sensor in the
world:
human eye

10. …and human ear:
vibration analysis

11. Today: Sensors + Big Data + Analytics

12. One hope from Big
Data:
Massive amounts of
information will
substitute for poor
quality.
Not necessarily!
Data science will find
the 10 lightbulbs.

13. Can’t get
something
out of
nothing
This is what data science is not.

14. Prediction is about having correct information.
Place relevant sensors at right places.
find the crack when it is small

15. Each PM problem is unique.
There is no one-approach-fits-all in PM.

16. But PM is
not only
about
predicting.

17. PM is a combination of:
Understanding the economics of the
industrial process
Discovering the proper sources of information
Creating a predictive model

18. Economics:
costs increase with PM effort;
number of sensors,
quality of prediction, etc.
as a result
savings and
profits rise,
but not linearly
sweet spot
PM costs are low,
savings are large
PM can be more expansive than savings

19. The person to askSourcesof
information

20. Reserve time for experimenting.
You will encounter failures before success.

21. Finally, a data scientist can create a model.
Remember: machine learning is a tool, not a solution.

22. Putting all aspects of PM together
is a collaborative work.

23. in conclusion

24. Predictive Maintenance is as much
research as it is data handling.
It is rather a process
and a mindset.
It has no single best practice.

25. Data Science
Team
Prepared by: Danko Nikolic & Davor Andric