Equipment fails on no schedule. Maintenance plans shouldn't either.
Most industrial maintenance still runs on two modes: react after failure, or replace on a calendar regardless of actual machine condition. Both are expensive. Reactive maintenance means unplanned downtime at the worst possible time. Time-based PM means replacing parts that didn't need replacing while missing the ones that did.
Neither approach uses the data the machines are already producing.
Maintenance driven by machine condition,
not the calendar.
We build condition-based monitoring systems that instrument your equipment, establish normal operating signatures, and flag degradation as it develops — before it becomes a failure event.
When something needs attention, maintenance teams get routed with context: what changed, how fast it's degrading, and what the likely cause is.
How it runs.
Sensors connected
Vibration, temperature, pressure, current draw, and flow data collected from equipment — via new instrumentation or existing historian connections.
Baselines established
Normal operating signatures defined per machine, load condition, and operating mode. What healthy looks like gets modeled, not assumed.
Anomalies detected
Deviations from baseline identified in real time. Rate of change tracked — a slow drift means something different than a sudden shift.
Severity scored
Each anomaly classified: monitor, warning, or action required. Thresholds set per asset based on criticality and lead time.
Work order triggered
CMMS notified automatically with context — what changed, when it started, and the probable component. Technicians arrive knowing what to look for.
Outcomes fed back
Repair records and failure confirmations improve detection over time. Failure patterns become institutional knowledge instead of tribal memory.
What gets built.
Instrumentation Layer
- Sensor selection and wiring
- OPC-UA / MQTT data collection
- Historian and SCADA connectivity
- Edge hardware where needed
Detection Engine
- Anomaly detection models per asset class
- Degradation rate tracking
- Failure mode pattern recognition
- Threshold configuration by criticality
Maintenance Layer
- CMMS work order integration
- Technician notification routing
- Asset health dashboards
- Failure history and trend reporting
What changes when this runs.
Reactive failures reduced through earlier, more specific detection
PM intervals based on actual machine condition instead of fixed schedules
Maintenance teams dispatched with context, not just an alarm
Unplanned downtime becomes a predictable, manageable cost
Equipment failure patterns preserved as data instead of anecdote
Critical assets monitored continuously without additional headcount
A full plant reliability platform.
Predictive maintenance on individual assets compounds. Once the instrumentation and detection layer is in place, the same infrastructure supports fleet-wide health scoring, parts inventory optimization, and maintenance scheduling integrated with production plans.
It starts with knowing which machine is about to fail. Everything else follows from there.
Running industrial equipment?
Tell us what assets you're most concerned about and what data you already have. We'll scope out a condition monitoring system from there.