Process-Data Analytics

Condition-monitoring technique · Updated 2026-07-15

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 in practice

Process-Data Analytics is versatile because it works across many asset types: heat exchangers, boilers, chillers and refrigeration and others. This breadth is both a strength and a consideration — a wide-ranging technique often requires less customization, but may not be as specialized as a dedicated point-solution. Most plants use Process-Data Analytics in combination with other techniques to build a complete condition-monitoring programme.

In practice: Process-Data Analytics excels at catching developing faults early, when they show as subtle changes in the monitored signal. The challenge is distinguishing a real fault signal from noise. Successful Process-Data Analytics programmes typically combine threshold alarms (alert if the signal exceeds a limit) with trending analysis (alert if the signal is rising fast, even if still below the limit). Both approaches matter for reliability.

Getting started: Implement Process-Data Analytics on your most critical assets first — those whose failure causes the longest downtime or highest cost. Start with one or two assets to learn the signals on your equipment and processes, then expand. Many plants find that Process-Data Analytics baseline data (what 'normal' looks like) takes 2–4 weeks to establish, after which the technique pays for itself through early fault detection.

Process-Data Analytics by equipment

Glossary: Process-Data Analytics →