Test of LFS upload and usage
Data set contains vehicle measurement of two different cars. All data are scaled and normalized. The measurement is split in time steps. Every row contains the data of this time step.
- Labels: Descriptors of the acceleration of the undamped mass (e.g. min, max, std)
- Features: vehicle speed, street roughness, ...
| amax | amin | std | kurtosis | skew | speed | AVG_IRI | ROUTE_TYPES | URBAN | BRIDGE | car | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.092182316 | -0.030727439 | 0.034474375873403615 | -1.248572544956299 | -0.32377647814911326 | 0.059264000000000004 | 4.8 | 0.0 | 1.0 | 0.0 | B |
| 1 | 0.092182316 | -0.061454877 | 0.031904225782007696 | -0.4145427908147341 | 1.0070682910778772 | 0.050004 | 4.8 | 0.0 | 1.0 | 0.0 | B |
| 2 | -0.030727439 | -0.092182316 | 0.014341965511269952 | -0.014041240938921007 | -0.9282224991843611 | 0.02778 | 4.8 | 0.0 | 1.0 | 0.0 | B |
| 3 | 0.061454877 | -0.092182316 | 0.021873665339597604 | 1.7844630674693747 | 1.5318499207812712 | 0.02778 | 4.8 | 0.0 | 1.0 | 0.0 | B |
| 4 | 0.04096992 | -0.061454877 | 0.026342002176476125 | -0.9213735255263735 | 0.9485480204179958 | 0.05556 | 4.8 | 0.0 | 1.0 | 0.0 | B |
| 5 | 0.030727439 | -0.061454877 | 0.01923562380743987 | 3.976726806984951 | 1.971356610418038 | 0.016668 | 4.8 | 0.0 | 1.0 | 0.0 | B |
| 6 | 0.21509208 | -0.21509208 | 0.04804132996888621 | 1.7082873112498378 | 0.07830243066921876 | 0.016668 | 4.8 | 0.0 | 1.0 | 0.0 | B |
Data set contains a acceleration measurement of a car. Sensors are placed on the unsprung mass. The dictionary contains data frames with the
- Acceleration signals (
$f_s=4096$ Hz) in x,y,z direction - Velocity of the car
- gps data
- street information
This data set contains acceleration measurements from 4 ball bearing endurance test runs.
- The data set comprises measurements of 4 similar bearings (#012, #019, #021, #024).
- All test runs are performed at a test bench with equal and constant operating conditions.
- The acceleration signals (
$f_s=20000$ Hz) are acquired with a triaxial accelerometer (accX, accY, accZ). - Each row of the pandas DataFrames contains
$1$ s of measurement. - The temporal distance between the individual rows of measurement is constant, but rescaled, so that the longest experiment comprises
$100$ imaginary time units. - The amplitudes of the measurements are standardized (mean
$= 0$ ; std$= 1$ ) for each measurement channel (accX, accY, accZ) individually.
