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Dataset Open Access

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

Klein, Steffen

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        <foaf:name>Klein, Steffen</foaf:name>
    <dct:title>Sensor data set, electromechanical cylinder at ZeMA testbed (2kHz)</dct:title>
    <dct:issued rdf:datatype="">2018</dct:issued>
    <dcat:keyword>dynamic measurement, measurement uncertainty, sensor network, digital sensors, MEMS, machine learning</dcat:keyword>
    <dct:issued rdf:datatype="">2018-08-02</dct:issued>
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    <dct:description>&lt;p&gt;&lt;strong&gt;General information on the data set&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;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.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Structure of the data&lt;/strong&gt;&lt;/p&gt; &lt;ul&gt; &lt;li&gt;data saved in HDF5 file as a 3D-matrix&lt;/li&gt; &lt;li&gt;one row represents one second of the return stroke of one working cycle (6292 rows: 6292 cycles)&lt;/li&gt; &lt;li&gt;one column represents one datapoint of the cycle, that is resampled to 2 kHz (2000 columns)&lt;/li&gt; &lt;li&gt;one page represent one sensor (11 pages: 11 sensors)&lt;/li&gt; &lt;/ul&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Allocation of the pages to the sensors&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;page 1: microphone&lt;br&gt; page 2: acceleration plain bearing&lt;br&gt; page 3: acceleration piston rod&lt;br&gt; page 4: acceleration ball bearing&lt;br&gt; page 5: axial force&lt;br&gt; page 6: pressure&lt;br&gt; page 7: velocity&lt;br&gt; page 8: active current&lt;br&gt; page 9: motor current phase 1&lt;br&gt; page 10: motor current phase 2&lt;br&gt; page 11: motor current phase 3&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Remark&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;The datasets are not in SI units. For conversion, you can use the PDF documentation.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Further information&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;For an introduction and tutorial to this data, a set of Jupyter notebooks is available &lt;a href=""&gt;here&lt;/a&gt;. 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.&lt;/p&gt;</dct:description>
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