Summary: The data set addresses the condition assessment of a hydraulic test rig based on multi sensor data. Four fault types are superimposed with several severity grades impeding selective quantification.
| Parameter | Value |
|---|---|
| Name | Condition monitoring of hydraulic systems |
| Labeled | Yes |
| Time Series | Yes |
| Simulation | No |
| Missing Values | No |
| Dataset Characteristics | Multivariate, Time-Series |
| Feature Type | Real |
| Associated Tasks | Classification, Regression |
| Number of Instances | 2205 |
| Number of Features | 43680 |
| Date Donated | 2018-04-25 |
| Source | UCI Machine Learning Repository |
The data set was experimentally obtained with a hydraulic test rig. This test rig consists of a primary working and a secondary cooling-filtration circuit which are connected via the oil tank [1], [2]. The system cyclically repeats constant load cycles (duration 60 seconds) and measures process values such as pressures, volume flows and temperatures while the condition of four hydraulic components (cooler, valve, pump and accumulator) is quantitatively varied.
Hydraulic systems, Condition monitoring, Sensor data, Time-series data, Mechanical systems