Clusters of sensors, the moniteur collects relevant data (vibration spectrum, temperature, acoustic emissions, etc.). This data is then analyzed in order to detect and anticipate breakdowns.Brochure
Replicating an instrumentation model or, more generally, a use case at the scale of an entire factory will help you capitalize on our experience and thus reduce or even eliminate long machine learning process. The goal: Deploy the most suitable predictive maintenance solution.
In a few seconds, the monitors are installed on the machine to be supervised.
Once acquired, the data is analyzed by automatically integrating generated thresholds.
Our goal: to enable you to act early enough to avoid heavy and costly breakdowns,but never too early to avoid unnecessary expenses and shutdowns.
Our kits are industrial IoTs, easy to deploy and connected to our cloud or to your own private cloud (Azure, AWS, etc.). The data generated is analyzed to anticipate failures of the machines on which you will install them.Online Demo
To try out real-time data feedback and conditional maintenance on a critical machine.
To try predictive maintenance on a critical machine or group (on the same site).