Mimir’s Anomaly Detection engine reviews all your equipment data 24/7 to flag issues across your fleet.
Instead of having simple threshold alarms (for example: “Temperature exceeds 60º”), Mimir account for all operating data ( for example: “Temperature outlier, accounting for ambient temperature, vibration, and frequency”). These “alarm boundaries” are continually evolving and improving as the models receive more equipment data.
We apply cutting-edge machine learning methods to classify your equipment’s operation into one of many unobservable operating states.
By analyzing historical operating data and looking for patterns, our models can identify any number of normal operating and failure modes.
SEE HOW MACHINE LEARNING TRANSFORMS ASSET MANAGEMENT