Know about machine failures weeks in advance
Predicta listens to your critical assets through vibration, temperature and current sensors. AI spots the fault while it is still small and schedules the repair before the line stops.
Good, warning or bad, for every machine
One list of every monitored asset with its condition and last communication time, so the maintenance team starts each day with the machines that need them.
- Asset health index and trend per machine
- Communication status for every sensor
- Condition counts for the whole plant
Thresholds that warn before they trip
Live gauges for vibration, temperature, current and pressure, each with warning and critical limits, so an operator sees a parameter drifting long before an alarm stops the machine.
- Vibration with FFT spectrum analysis
- Bearing temperature and motor current signature
- Anomaly and threshold alerts by app, SMS and email
Trends that show the failure coming
Predicta compares each signal with the asset's learned normal pattern and estimates its remaining useful life, so the repair goes into a planned stop instead of the middle of a shift.
- Remaining useful life (RUL) estimates
- P-F interval warnings for developing defects
- Planned work order raised automatically in FixItNow
A planned repair costs a fraction of a breakdown
A bearing replaced in a planned stop takes hours and a spare. The same bearing failing mid-shift can take a spindle, a batch of parts and a delivery date with it.
- Spindles, motors, pumps, compressors, gearboxes and fans
- Hydraulic oil temperature, pressure and relay health
- Works with FixItNow and Efficio on one platform
What is condition-based maintenance?
Condition-based maintenance services a machine when its measured condition shows it is needed, instead of on a fixed calendar or after it breaks. Predicta measures vibration, temperature and current continuously to decide when that is.
How early can Predicta detect a failure?
Typically 1 to 4 weeks before failure, in the P-F interval when a defect such as bearing wear, misalignment or imbalance has started but the machine still runs.
How accurate are the predictions?
Predicta's fault detection is 85 to 95 percent accurate in typical deployments. Accuracy improves as the model learns each asset's normal behaviour over the first weeks.
Which machines benefit most?
Critical rotating assets whose failure stops a line: spindles, motors, pumps, compressors, gearboxes, fans and blowers, as well as process parameters such as hydraulic oil temperature and pressure.
Works even better together
All four products share one platform, one login and the same machine data. Add the next one any time.
Get a free plant study and a personalised solution plan
Our engineers visit your plant, study your machines and goals, and recommend the right mix of automation, robotics and Industry X.0, with an estimated return.