Every machine's vital signs, watched continuously - with the exact anomaly flagged before it becomes a breakdown.
Misamtek streams real-time sensor data from your factory equipment, detects anomalies as they emerge, and recommends maintenance from what the equipment is actually doing - not a fixed calendar.
Breakdowns don't arrive. They drift in.
Every failed machine had a signature building for hours or days. Reading a sensor stream once a shift is not monitoring. It is a slower version of finding out too late.
A vibration pattern shifts for days
A bearing on its way out sends the same signal for a week before the shift it fails on. Almost no one is watching that closely.
Sensor data gets logged, not watched
Historians collect terabytes. Alarms fire only on hard limits. The slow drift in the middle is invisible to anyone with a job to do.
Maintenance runs on the calendar
A checklist every 30 days catches what a checklist catches. What the equipment is actually saying today is a different question.
Four stages, one continuous stream
A live signal in, a diagnosed callout out. No dashboards to interpret before something acts.
Stream
Sensor data from every connected machine ingests continuously. Time-series, high frequency, per-machine.
Detect
Learned envelopes per machine. The moment a reading drifts outside its own normal pattern, it is flagged.
Diagnose
A likely cause is suggested from the specific anomaly signature - bearing wear, misalignment, valve fault - not a generic alarm.
Predict
A service window is recommended from actual behavior, cross-referenced with failure patterns on similar equipment.
Concrete work, not a chart wall
Four capabilities that operate on every stream, on every connected machine, continuously.
Real-time anomaly detection
Every reading is scored against a learned envelope for that specific machine. Drift outside pattern is flagged the moment it happens.
Diagnostic suggestions
From the shape of the anomaly - not just its magnitude - a likely cause is proposed: bearing wear, misalignment, valve fault, thermal loop.
Predictive maintenance timing
Service windows are recommended from the equipment's own behavior, not a fixed calendar. Windows narrow as the anomaly resolves.
Cross-machine pattern learning
Failure signatures seen on similar equipment elsewhere in the fleet inform detection everywhere else.
Different equipment. Same continuous signal.
Floor-wide equipment health, fewer unplanned stoppages.
Full visibility across every connected machine on the floor. Anomalies flagged before the shift they would have stopped. Maintenance windows built around real behavior.
Ship your equipment with a monitoring layer built in.
Offer customers real-time health, anomaly flagging and diagnosed callouts as part of the machine you sell. Deployed under your brand, aligned with your service model.
The signals we read
The four channels that carry the earliest signs of trouble on rotating and reciprocating equipment.
Temperature
Bearing, winding, discharge, oil - the first channel to drift on a thermal-loop failure.
Vibration
The primary early indicator on rotating equipment. Amplitude and spectral shift both watched.
Pressure
Discharge and differential pressures scored per stage on compressors and pumps.
Power draw
Current signature analysis picks up loading and mechanical shifts before mechanical sensors see them.
Monitoring, not sampling.
Pick an equipment type. Watch its sensor pulse move.
This is not a mockup. The waveforms below are being generated the same way we score real streams - learned envelope, drift detection, diagnosed callout. Pick a machine and watch an anomaly emerge.