Process-Data Analytics for chillers and refrigeration
Process-Data Analytics is one of the most effective ways to monitor chillers and refrigeration: it catches developing faults — condenser and evaporator fouling, refrigerant undercharge, overcharge or leaks, compressor bearing and valve wear — early, so repairs are planned rather than forced by a breakdown.
Why process-data analytics suits chillers and refrigeration
Chillers and refrigeration are major electricity users whose efficiency degrades quietly with condenser/evaporator fouling, refrigerant charge problems and compressor wear. Because cooling is often critical to process or product, an unplanned chiller trip is costly — and the early signs are clear in performance and vibration data.
How process-data analytics 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.
Faults it catches on chillers and refrigeration
- Condenser and evaporator fouling
- Refrigerant undercharge, overcharge or leaks
- Compressor bearing and valve wear
- Lubrication problems and oil contamination
- Control and sensor faults
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 on chillers and refrigeration: implementation
What the signal looks like. 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.
How that reads on chillers and refrigeration specifically. A widening approach temperature and rising kW-per-ton at constant load signal fouling or charge problems; compressor vibration and oil-debris trends reveal mechanical wear. Trending efficiency shows the cost-justified time to clean or service.
Where it is blind. Process-Data Analytics does not cover every failure mode on chillers and refrigeration. The equipment profile also lists Performance analytics (approach temperatures, kW/ton, efficiency trending), Vibration analysis on compressors, Oil analysis — they exist because they detect modes this technique cannot see.
Before you set a threshold. Baselining, alarm criteria and the route from alert to work order are the same problem for every technique. They are covered once in setting up a condition monitoring programme and in the process-data analytics overview.
Process-Data Analytics on other equipment
Process-Data Analytics for heat exchangers · Process-Data Analytics for boilers · Process-Data Analytics for cooling towers
Related
Predictive maintenance for chillers and refrigeration · Process-Data Analytics overview · Process-Data Analytics