Thingsform IoT
IoT use case
Predictive maintenance
Machine health & anomaly detection
Vibration
8.4mm/s
0.212.0 mm/s
Bearing temp
75°C
4595 °C
Cycle hours
4 714h
1008 500 h
Anomaly score
11%
0100 %
30devices
28online
3sites
2 devices offline: supervision flags it rather than hiding it.
How it is wired
Sensors report to a gateway, which forwards to Thingsform over MQTT. What sets the platform apart is the lower line: the command goes back to the equipment.
- On site
- 30 devices · 3 sites
- Link
- MQTT · HTTP · Modbus
- Tracked readings
- Vibration · Bearing temp · Cycle hours · Anomaly score
What the platform does with these readings
The dashboard is only the visible part: each metric can carry a threshold, drift detection and a command in return.
Vibration
0.2–12.0 mm/s- Threshold outside the 0.2–12.0 mm/s range
- Slow drift, spotted before the threshold is crossed
- Command sent back to the device over MQTT or Modbus
Bearing temp
45–95 °C- Threshold outside the 45–95 °C range
- Slow drift, spotted before the threshold is crossed
- Correlation with the site's other readings
Cycle hours
100–8 500 h- Threshold outside the 100–8 500 h range
- Slow drift, spotted before the threshold is crossed
- Correlation with the site's other readings
Anomaly score
0–100 %- Threshold outside the 0–100 % range
- Slow drift, spotted before the threshold is crossed
- Correlation with the site's other readings
Start with the Predictive maintenance dashboard
It ships configured. Connect your sensors over MQTT, HTTP or Modbus, and your values take the place of these.