Vibration Measurements on a Rotating Shaft at Different Unbalance Strengths
---
Oliver Mey, Willi Neudeck, André Schneider and Olaf Enge-Rosenblatt  
Fraunhofer IIS/EAS, Fraunhofer Institute for Integrated Circuits  
Division Engineering of Adaptive Systems  
Dresden, Germany  
oliver.mey@eas.iis.fraunhofer.de  
---

### General Description

This dataset contains vibration data recorded on a rotating drive train. This 
drive train consists of an electronically commutated DC motor and a shaft driven
by it, which passes through a roller bearing. With the help of a 3D-printed
holder, unbalances with different weights and different radii were attached to
the shaft. Besides the strength of the unbalances, the rotation speed of the
motor was also varied.

This dataset can be used to develop and test algorithms for the automatic
detection of unbalances on drive trains. Datasets for 4 differently sized
unbalances and for the unbalance-free case were recorded. The vibration data was
recorded at a sampling rate of 4096 values per second. Datasets for development
(ID "D[0-4]") as well as for evaluation (ID "E[0-4]") are available for each
unbalance strength. The rotation speed was varied between approx. 630 and 2330
RPM in the development datasets and between approx. 1060 and 1900 RPM in the
evaluation datasets. For each measurement of the development dataset there are
approx. 107min of continuous measurement data available, for each measurement of
the evaluation dataset 28min.

#### Overview of the dataset components:
| ID	 |	Radius [mm]  | Mass [g] |
|--------|-------------------|----------|
| 0D/ 0E | -		     | -        |
| 1D/ 1E | 14		     | 3.281    |
| 2D/ 2E | 18.5	             | 3.281    |
| 3D/ 3E | 23		     | 3.281    |
| 4D/ 4E | 23		     | 6.614    |

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### Technical Description

The dataset contains one csv file per measurement. All files are packed into one
zip-file. The files can, for example, be read using Python and the modules
zipfile and pandas. An example code for this follows:

~~~~~
import pandas as pd
import zipfile

with zipfile.ZipFile(filename, 'r') as f:
    with f.open('0D.csv', 'r') as c:
        data0D = pd.read_csv(c)
    with f.open('0E.csv', 'r') as c:
        data0E = pd.read_csv(c)
    with f.open('1D.csv', 'r') as c:
        data1D = pd.read_csv(c)
    with f.open('1E.csv', 'r') as c:
        data1E = pd.read_csv(c)
    with f.open('2D.csv', 'r') as c:
        data2D = pd.read_csv(c)
    with f.open('2E.csv', 'r') as c:
        data2E = pd.read_csv(c)
    with f.open('3D.csv', 'r') as c:
        data3D = pd.read_csv(c)
    with f.open('3E.csv', 'r') as c:
        data3E = pd.read_csv(c)
    with f.open('4D.csv', 'r') as c:
        data4D = pd.read_csv(c)
    with f.open('4E.csv', 'r') as c:
        data4E = pd.read_csv(c)
~~~~~