Sensor data set, electromechanical cylinder at ZeMA testbed (2kHz)

<p><strong>General information on the data set</strong></p> <p>The data set was generated at the ZeMA testbed. A working cycle lasts 2.8s and consists of a forward stroke, a waiting time and a return stroke. The data set does not consist of the entire working cycles. Only one second of the return stroke of each working cycle is used.</p> <p>&nbsp;</p> <p><strong>Structure of the data</strong></p> <ul> <li>data saved in HDF5 file as a 3D-matrix</li> <li>one row represents one second of the return stroke of one working cycle (6292 rows: 6292 cycles)</li> <li>one column represents one datapoint of the cycle, that is resampled to 2 kHz (2000 columns)</li> <li>one page represent one sensor (11 pages: 11 sensors)</li> </ul> <p>&nbsp;</p> <p><strong>Allocation of the pages to the sensors</strong></p> <p>page 1: microphone<br> page 2: acceleration plain bearing<br> page 3: acceleration piston rod<br> page 4: acceleration ball bearing<br> page 5: axial force<br> page 6: pressure<br> page 7: velocity<br> page 8: active current<br> page 9: motor current phase 1<br> page 10: motor current phase 2<br> page 11: motor current phase 3</p> <p>&nbsp;</p> <p><strong>Remark</strong></p> <p>The datasets are not in SI units. For conversion, you can use the PDF documentation.</p> <p>&nbsp;</p> <p><strong>Further information</strong></p> <p>For an introduction and tutorial to this data, a set of Jupyter notebooks is available <a href="https://github.com/harislulic/ZeMA-machine-learning-tutorials">here</a>. These notebooks contain Python code and a documentation of example machine learning tasks and analysis of this data set. In the near future, these will be extended to also include uncertainties in the input data.</p>