Using mathematical models and algorithms that use real-time data to anticipate future downtime events, our platform gives clients time to invoke countermeasures to proactively prevent plan and prevent unscheduled outages and possible downtime.

In this client example, we use the sensor data to ‚Äčtrain‚Äč a model on the correlations between a signal pattern and the subsequent downtime events.

Once trained, the model can then be presented with a set of signals and, based upon its training, indicate whether a downtime event is imminent or not and the likelihood of the event.

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