Sensor monitoring
Scattered sensors are brought into one structure and
anomalies are read together, with none missed
Why it matters
Every machine brings its own sensors and protocols, so the data cannot be read in one place.
Each was added when it was needed, which left the formats and collection methods all different.
Scattered signals make it hard to judge an anomaly as a whole.
The data and signals we read
Five principles for turning industrial data into an assetHow Refinery solves it
Multi-sensor collection
From IoT sensors to existing instrumentation, diverse sources are standardized and gathered in one place.
Connection through the ontology
Each signal is given meaning — which asset it belongs to and what it measures — so it can be interpreted alongside the others.
Real-time anomaly monitoring
Multiple signals are seen on one screen, and combinations that differ from the norm are caught.
Related use cases
Early signs of motor failure are read with wireless vibration sensors, cutting stoppages and losses.
When, where and how much you use is identified, cutting power cost and risk.
Power quality anomalies you cannot see are caught in real time, and put on the record.
The whole plant’s energy flow is brought into one view, cutting waste and emissions together.
Hidden waste and power peaks are found, and the same work is done for less.
Signals from the process data are read, and defects are caught before the part is finished.
