Multi-sensor monitoring
Bring scattered sensors into one structure
and read anomalies together
Why it matters
Every machine brings its own sensors and protocols, so the data never reads from 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.
Data and signals
Five principles for turning industrial data into an assetHow Refinery solves it
Collect from many sources
From IoT sensors to existing instrumentation, diverse sources are standardized and gathered in one place.
Connect 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.
Watch in real time
See multiple signals on one screen and catch combinations that differ from the norm.
Related use cases
Read the early signs of motor failure
and cut unplanned downtime.
Know when, where and how much you use,
and cut cost and risk.
Catch power quality faults you cannot see,
in real time, on the record.
Bring the whole plant’s energy into one view
and cut waste and emissions together.
Find the hidden waste and the peaks,
and do the same work for less.
Read what the process data is signalling
and catch defects before they finish.
