Solution
Spot failures before they happen
A sensor on a pump, motor or conveyor and a model that knows normal operation report anomalies early. Your maintenance data becomes a failure risk.
The problem
A pump, a compressor, a conveyor: when it fails, work stops, and the repair comes at the worst possible time. Maintenance happens by the calendar or when something sounds odd. The first is often too early, the second too late.
The solution
Two ways, depending on which data you have:
- Monitor: a model learns how the machine sounds or vibrates in normal operation. If something deviates clearly, you get a message, even for faults it has never seen before.
- Forecast: from running times, measurements and the maintenance log a model learns which combinations precede a failure and shows the risk per machine.
- Report: by e-mail or in your application, with the history of the measurements, so that you can decide
- Plan: maintenance when it is needed, not after the standstill
The demo application shows both: an industrial pump whose sound the model learns in seconds, and 10,000 machine runs from a public maintenance dataset on which it forecasts failures.
What it costs
In the free initial assessment I first find out which data exists and whether it is enough. If it pays off, a trial run on one machine at a fixed price follows, then the expansion in stages. More under AI with approval.
Frequent questions
Do I need data from earlier failures?
Not necessarily. For monitoring it is enough for the model to know normal operation. Anything that clearly deviates from it is reported. If there are maintenance logs with earlier failures, a failure risk can be forecast as well.
Which sensors do I need?
Often measurements already exist, for example from the machine's controller. If not, inexpensive vibration or sound sensors are enough to start with. I work out which ones fit with you on site or from photos.
How many false alarms are there?
I tune that with you. With failures both count - how many are caught and how often an alarm is raised for nothing. In the trial run you see both figures before monitoring goes live.