Predictive Maintenance

Detect equipment failure before it happens and streamline your maintenance operations using machine learning automation on


Detecting Equipment Failure

Being able to identify and prevent equipment failure is a crucial challenge for organizations in energy, mobility, manufacturing. Traditional preventive maintenance requires a pre-established schedule based on time or equipment use. However, these methods systematically lead to unexpected failures because repairs are planned and do not estimate the actual condition of equipment. This results in very high maintenance costs and reduced efficacy of maintenance operators.


Making Maintenance Predictive

Predictive maintenance based on Machine Learning algorithms correlates data from various sources (sensors, IoT, meteorological conditions) to identify failure patterns. This technique aims to better predict which components should be replaced before they break down. Predictive models can be deployed on the equipment itself and generate more accurate predictions without requiring any online connection. Maintenance teams take preemptive measures on equipment that need repair, and managers can optimize production.

Using for Predictive Maintenance provides teams with an automated platform to quickly build and deploy machine learning models according to your enterprise data and target. It produces custom models based on your equipment data and facilitates deployment of models on the machine itself. Identify failure before it happens and better scale your maintenance operations.

Thanks to, we have moved from traditional preventive maintenance to proactive and connected maintenance. We can now learn about train failures before the time and act accordingly.

Project Manager at the Material Department, Transport and Logistics Company

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