Our predictive maintenance software watches the vibration, temperature, current draw, and pressure signatures of your critical equipment around the clock, and flags a developing problem while it's still developing - not after a furnace, turbine, kiln, or compressor has already gone down mid-shift.
Reactive maintenance waits for a failure and then reacts to it - an emergency repair crew, a production stoppage, parts sourced under pressure, and a cost that's always higher than it needed to be. Predictive maintenance moves that same repair earlier, into a planned maintenance window chosen on your terms, because the equipment showed signs of needing attention before it actually failed. The repair itself doesn't change. When it happens does - and that timing is most of the cost difference, in any process industry.
Every piece of rotating or thermal equipment has a normal operating signature - a vibration profile at running speed, a temperature curve under load, a current draw at a given output, a pressure range at a given flow. We establish that baseline for each monitored asset from its own real operating data, not a generic manufacturer spec sheet. Once the baseline exists, a deviation from it - a bearing starting to run rough, a motor drawing more current than it should for the same load - becomes visible immediately, instead of staying invisible until it becomes a failure. The physics behind this doesn't change from one industry to the next; only the equipment does.
Sensors feed continuous readings into EpsumThings' condition-based monitoring capability, which compares live data against each asset's own baseline in real time. A deviation doesn't just trigger a generic alarm -- it's flagged with the specific parameter that moved, how far it moved, and how that pattern compares to what has historically preceded a failure on similar equipment, so the maintenance team knows what they're looking at before they walk over to check.
The same baseline-and-deviation approach applies across process industries -- what changes is which equipment matters and which parameters actually predict failure in that environment:
Furnace and reheating equipment temperature profiles, refractory condition, and burner performance; rolling mill bearing vibration and roll wear; continuous caster mould-level stability and cooling performance; crane load patterns and motor health; and the compressors, pumps, and fans that keep the rest of the shop running.
Turbine and generator vibration and bearing temperature, boiler tube and combustion performance, transformer and switchgear thermal condition, and the cooling and fuel-handling systems that keep a generation unit available when it's needed.
Kiln shell temperature and refractory condition, raw mill and cement mill bearing vibration, crusher and conveyor motor health, and the ID fans and compressors that keep the grinding and pyroprocessing lines running without an unplanned stop.
Pump and compressor vibration and bearing condition, pipeline pressure and flow deviations, rotating equipment (turbines, motors) thermal and vibration signatures, and the storage and transfer equipment that keeps product moving safely.
Vibration, temperature, current, and pressure sensors are installed on covered equipment and connected into EpsumThings, so readings flow into one platform rather than a collection of standalone gauges.
Each asset's normal operating signature is established from its own real running data before any alerting goes live, so what counts as "abnormal" is specific to that machine, not a generic threshold.
Live readings are compared against the established baseline in real time, watching for the specific deviation patterns that have historically preceded failure.
A deviation triggers an alert that includes what moved, by how much, and what it typically indicates — not just a threshold-crossed notification.
Alerts connect into FileGenix, so the maintenance team sees the equipment's actual service history and prior repairs alongside the new alert, instead of starting the investigation cold.
As more operating data and outcomes accumulate, baselines and alert thresholds get tuned to the specific behavior of your equipment, so accuracy improves over time rather than staying fixed at day-one settings.
Eliminates unplanned stoppages caused by equipment that failed without warning
Moves repairs into planned maintenance windows instead of emergency call-outs
Extends equipment lifespan by catching and correcting developing issues before they cause secondary damage
Reduces overall maintenance cost, since a scheduled repair is consistently cheaper than an emergency one
Improves safety records by catching equipment conditions that could become a hazard before they do
This runs on EpsumThings, the same platform behind our SCADA, energy monitoring, and asset-tracking deployments across steel, power, and process industries -- so predictive maintenance data sits alongside your production and energy data instead of living in a separate system. We've built this same baseline-and-deviation approach before, in a steel plant's thermal health monitoring system for ladle refractory condition -- the same underlying logic applies to a turbine bearing, a cement kiln shell, or a compressor in an oil & gas facility: understand what normal looks like for a specific asset, then flag the moment it stops looking normal. It's a machine failure prediction approach grounded in how your own equipment actually behaves, not a generic industry model.
If your maintenance team's first sign of a problem is usually the equipment stopping, that's exactly the gap predictive maintenance is built to close -- whatever the plant runs on.