Process-Data Analytics
Process-data analytics models the expected behaviour of equipment from existing historian and SCADA data — temperatures, pressures, flows, power — and flags deviations that signal degradation. It covers assets without dedicated condition sensors, such as heat exchangers, boilers and chillers.
How it works
Rather than adding sensors, analytics learns the normal relationship between a process and its conditions from existing data, then detects when reality drifts from expectation. A widening approach temperature, a rising pressure drop, a falling efficiency at constant load — all signal fouling, scaling or wear long before failure. It scales across many assets cheaply, depending on the quality of the existing data.
What the data shows
A widening approach temperature and rising pressure drop on a heat exchanger or chiller signal fouling; a rising stack temperature on a boiler signals fouling or scaling; a falling efficiency at constant load on any thermal asset signals degradation.
Process-Data Analytics by equipment
Process-Data Analytics for heat exchangers
Faults it catches on heat exchangers and what the data shows.
Process-Data Analytics for boilers
Faults it catches on boilers and what the data shows.
Process-Data Analytics for chillers and refrigeration
Faults it catches on chillers and refrigeration and what the data shows.
Process-Data Analytics for cooling towers
Faults it catches on cooling towers and what the data shows.