Short answer
Sensors are installed on equipment to measure, for example, running time, current, temperature, vibration or output. The data is sent to a system that shows downtime, utilisation and anomalies, and alerts are sent. Over time the data can be used to predict failures.
What can be measured
- Running time and downtime — is the machine working, and for how long?
- Power consumption — utilisation and unusual behaviour.
- Temperature and vibration — early signs of failure.
- Output and scrap.
- Environmental conditions — temperature and humidity in rooms or the warehouse.
How it works
- Sensors on the equipment — often they can be added to older machines without rebuilding them.
- Data transfer to the cloud or a server.
- Dashboard — the status of equipment in real time.
- Alerts — when a machine stops or a reading crosses a threshold.
- Analysis — trends, efficiency, planned maintenance.
The next step — predictions
Once data has been collected, machine learning can predict when a machine is likely to need maintenance. But start with the simplest thing — visibility and alerts. More on manufacturing: how to automate manufacturing.