ARTIFICIAL INTELLIGENCE APPLIED TO

Predictive Maintenance

PROBLEM

One of the main cost in Industrial sector, comes from mechanical components maintenance. To ensure smooth and uninterrupted functioning, companies use to be conservative in terms of their equipment part renewal, following manufacturer recommendations based on averages. The conservative approach tends to increase maintenance costs beyond real needs.

We develop a Terminus7 machine module dedicated to this use case of components lifespan prediction based on use patterns.

CHALLENGE

Maintenance costs higher than real needs

SOLUTION

We have collaborated with several of our customers of the Industrial Sector to test our Terminus7 Predictive Maintenance module. We trained T7 Machine with past failures’ data and in-life usage patterns of each part, in order to correlate both information sources with Machine Learning techniques.

After Terminus7 training, in each case we tested our solution during several months. Terminus7 demonstrates to be able to predict single component failure, helping to extend component’s life beyond its end-of-life or anticipating breaks before they happened.

The final prediction quality of Machine Learning strongly depends on data quality and volume in order to be able to use the most advanced models and techniques.

To connect usage patterns with component lifespan is a typical and very valuable use case of machine learning into the Industrial sector.

CHALLENGE SOLVED

Predict single component failure based on usage patterns

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