Predictive analysis
Know which machine is likely to fail, and why.
Predictive analysis looks at the trends in your machines' readings and forecasts where they are heading. It tells you which machines are at risk and explains the reasons in plain words.
How it works
Forecasts, risk, and the reasons behind them.
| Part | What it does |
|---|---|
| Trend forecast | Projects each key reading 7 to 30 days ahead using a time-series model (ARIMA), with a range showing how sure it is. |
| Failure risk | A machine-learning model (Random Forest) weighs readings such as vibration, temperature, motor current and recent downtime, and gives each machine a risk score. |
| Plain-English reasons | Each risk score comes with the readings that drove it, written in words, for example: "higher than usual bearing temperature and vibration variability". |
| Weekly run | The models re-run on a schedule against the latest history, so forecasts stay current. |
Honest about data
It learns from your plant, not from a brochure.
- Your machines' own historyModels are trained on your plant's readings, not on sample data from somewhere else.
- Needs time to learnPredictions start once there are a few months of history for a machine, and get better as more builds up.
- Explains itselfEvery risk comes with its reasons, so a maintenance engineer can check it rather than trust a black box.
- Shown where you workPredictive alerts appear on dashboards through the Predictive alert widget.
See Nisthora on your own machines.
Book a demo. We will show you live data and talk through your machines, lines and shifts.
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