May 10, 2019 Dataset Open Access

Dorst, Tanja

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    "description": "<p><strong>General information on the data set</strong></p>\n\n<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>\n\n<p>&nbsp;</p>\n\n<p><strong>Structure of the data</strong></p>\n\n<ul>\n\t<li>data saved in three HDF5 file as a 3D-matrix, one file is for one axis</li>\n\t<li>one row represents one second of the return stroke of one working cycle<br>\n\taxis 3: 6292 cycles<br>\n\taxis 5: 6083 cycles<br>\n\taxis 7: 5732 cycles</li>\n\t<li>one column represents one datapoint of the cycle, that is resampled to 2 kHz (2000 columns)</li>\n\t<li>one page represent one sensor (11 pages: 11 sensors)</li>\n</ul>\n\n<p>&nbsp;</p>\n\n<p><strong>Allocation of the pages to the sensors</strong></p>\n\n<p>page 1: microphone<br>\npage 2: acceleration plain bearing<br>\npage 3: acceleration piston rod<br>\npage 4: acceleration ball bearing<br>\npage 5: axial force<br>\npage 6: pressure<br>\npage 7: velocity<br>\npage 8: active current<br>\npage 9: motor current phase 1<br>\npage 10: motor current phase 2<br>\npage 11: motor current phase 3</p>\n\n<p>&nbsp;</p>\n\n<p><strong>Remark</strong></p>\n\n<p>The datasets are not in SI units. For conversion, you can use the PDF documentation.</p>\n\n<p>&nbsp;</p>\n\n<p><strong>Further information</strong></p>\n\n<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>", 
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    "title": "Sensor data set of 3 electromechanical cylinder at ZeMA testbed (2kHz)", 
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    "keywords": [
      "dynamic measurement, measurement uncertainty, sensor network, digital sensors, MEMS, machine learning, European Union (EU), Horizon 2020, EMPIR"
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    "publication_date": "2019-05-10", 
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