Reasons Why You Should Use Machine Learning for Predictive Maintenance6

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1. Only do maintenance

when you need to

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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.

2. Stop wasting money on

unnecessary work

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The combined cost of excess maintenance and lost productivity in the US has been estimated at $740B.

3. Keep your assets in

production

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Using predictive instead of preventative maintenance means no more shutting down assets that could be working.

4. Scale without needing

to hire more experienced personnel

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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.

5. Reduce unplanned

downtime by resolving issues prior to failure

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Integrating machine learning predictions with your BPM or EAM system will help engineers respond to imminent failures faster.

6. Help engineers make

smarter decisions

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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.

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-Analytics2. https://www.gemeasurement.com/sites/gemc.dev/files/orbit_v31n12011-q1_anomalert_predictive_maintenance.pdf

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