Public (anonymized) predictive maintenance datasets from Huawei German Research Center.
Predictive Maintenance Datasets
- Datasets from a variety of IoT sensors for predictive maintenance in elevator industry. The data is useful for predictive maintenance of elevators doors in order to reduce unplanned stops and maximizing equipment life cycle.
- The dataset contains operation data, in the form of timeseries sampled at 4Hz in high-peak and evening elevator usage in a building (between 16:30 and 23:30). For an elevator car door the system we consider: Electromechanical sensors (Door Ball Bearing Sensor), Ambiance (Humidity) and Physics (Vibration).
Some example data depicting the of the three sensors:
| ball-bearing (revolutions) | humidity (%) | vibration (dB) |
|---|---|---|
| 93.744 | 73.999 | 18 |
| 93.74 | 73.999 | 18.001 |
| 93.736 | 73.998 | 18.002 |
| 93.732 | 73.998 | 18.003 |
| 93.729 | 73.998 | 18.004 |
| 93.725 | 73.997 | 18.005 |
| 93.721 | 73.997 | 18.006 |
| 93.717 | 73.997 | 18.007 |
| 93.713 | 73.996 | 18.008 |
| 93.709 | 73.996 | 18.009 |
