Quick Links
Every industrial asset tells a story through its sensors. Trucision is built to listen. There's a moment that happens in every plant, every facility, every fleet. A technician walks past a piece of equipment that's been running fine for months. Hours later, it fails. The post-mortem always reveals the same thing: the warning signs were there in the data — vibration shifting, temperatures creeping, pressure ratios drifting — but nobody was watching the right signals at the right time. This isn't a staffing problem. It's a signal problem. And it's exactly what Trucision is designed to solve.
Modern industrial equipment generates enormous amounts of sensor data — often hundreds of readings per second, across dozens of channels. Most of it flows into historians or data lakes and stays there. The operations team gets dashboards with threshold alarms. The maintenance team responds when something breaks or when the calendar says it's time.
Neither approach uses what the data is actually telling you.
Trucision closes that gap. It turns continuous sensor streams into a living picture of your equipment's health — not just whether something is wrong right now, but how long you have before it becomes a problem, and what's driving it.
We don't just detect anomalies. We model degradation.
Most anomaly detection tools compare current readings against historical averages or fixed thresholds. They're reactive by design. They tell you something is wrong after it's already wrong.
Trucision's health modeling layer learns the trajectory of how your specific equipment degrades — not how equipment in general degrades. It understands that a compressor running at 40°C ambient in summer behaves differently than the same compressor in winter. It accounts for load cycles, operating regimes, and the accumulated history of how this particular asset has been run. From that, it builds a continuously updated health index: a single, interpretable signal that tells you where your equipment is in its degradation curve and where it's headed.
We respect the physics of your equipment.
Data-only models can learn patterns that look real but aren't. A purely statistical model might predict that a bearing gets healthier under certain load conditions simply because the training data happened to show that pattern. That's dangerous.
Trucision's core engine is built with the physics of degradation in mind. The health models we deploy understand that equipment wears in one direction. They're designed to produce forecasts that respect the fundamental mechanics of your assets — not just the statistics of your historical data. This means fewer false alarms, more confident predictions, and maintenance recommendations you can trust.
We give you time, not just alerts.
The output of Trucision isn't an alarm that says "something is wrong." It's a forecast: this asset has an estimated X operating hours remaining before maintenance is required. That forecast comes with a confidence window, and it updates continuously as new sensor data arrives. You can plan around it.
Every recommendation comes with a reason.
One of the reasons AI hasn't been adopted more broadly in maintenance-critical industries is trust. A system that says "replace this component" but can't explain why gets ignored — or worse, blindly followed. Trucision surfaces the specific sensor patterns and conditions that drove each recommendation, in plain language. Your operators can verify the reasoning. Your engineers can use it to improve equipment design. Your auditors have a record.
Unplanned downtime costs industrial operators an estimated 5–20% of productive capacity per year. Trucision's predictive horizon — updated continuously, specific to each asset — lets you convert emergency repairs into planned maintenance windows. The asset still gets fixed; it just gets fixed on your schedule.
Over-maintenance is the less-discussed twin problem. Fixed-interval schedules cause teams to replace components that have significant life remaining. Trucision shows you the actual condition of each asset, so you replace what needs replacing — not what the calendar says.
Incident investigation is slow, expert-dependent, and inconsistent. When something does go wrong, Trucision's root cause layer structures the investigation automatically, surfacing the most probable causal chains from sensor history and operational context. What used to take days of expert analysis takes minutes.
Knowledge retention is a growing challenge as experienced engineers retire. Trucision captures the implicit diagnostic knowledge that experienced operators use — the pattern recognition that tells them something "sounds wrong" or "doesn't feel right" — and makes it available to the whole team, not just the people who've been doing this for 30 years.
Trucision is designed for operations and maintenance leaders in industries where equipment reliability is a competitive and safety-critical concern: energy and utilities, oil and gas, manufacturing, process industries, aviation MRO, and transportation infrastructure.
It doesn't require a data science team to operate. It integrates with the sensor infrastructure and historian systems you already have. And it's built to work alongside your maintenance management system — not replace it.
Your equipment is already generating the data that could predict its next failure. The question is whether you have a system that can turn that data into a maintenance decision before the failure happens — not after.
That's what Trucision does.
Request a demonstration → See Trucision running on your instruments, without OEMs help or any firmware update to the instrument, in a live session with our engineering team.
