1.
Reasons Why You Should
Use Machine Learning for
Predictive Maintenance6
XMPro

2.
1.
Only do maintenance
when you need to
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3.
According to NASA, failure patterns that are
age related only apply to 18% of assets.
Using asset condition data and machine
learning algorithms to predict failures will
allow you to do maintenance when it matters.

4.
2.
Stop wasting money on
unnecessary work
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5.
The combined cost of excess
maintenance and lost productivity in
the US has been estimated at $740B.

6.
3.
Keep your assets in
production
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7.
Using predictive instead of preventative maintenance
means no more shutting down assets that could be working.

8.
4.
Scale without needing
to hire more experienced
personnel
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9.
Machine learning is easy to scale across
different types of assets, unlike some
condition monitoring techniques that are
expensive and require specialists for data
analysis.

10.
5.
Reduce unplanned
downtime by resolving
issues prior to failure
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11.
Integrating machine learning predictions with
your BPM or EAM system will help engineers
respond to imminent failures faster.

12.
6.
Help engineers make
smarter decisions
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13.
Combining asset failure predictions with process mining data
gives you insight into which actions create the best outcomes.
A machine learning algorithm can use this data to make
recommendations to engineers on the best action to take next.

14.
Learn How To Get Started
With Machine Learning For
Predictive Maintenance
Sources:
1. http://www.arcweb.com/Blog/Post/260/Proactive-Asset-Management-with-IIoT-and-Analytics
2. https://www.gemeasurement.com/sites/gemc.dev/files/orbit_v31n12011-q1_anomalert_predictive_maintenance.pdf
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