Dataset for Sensorless Drive Diagnosis
M. Bator, Dataset for Sensorless Drive Diagnosis, UCI Machine Learning Repository, 2013.
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Abstract
Features are extracted from electric current drive signals. The drive has intact and defective components. This results in 11 different classes with different conditions. Each condition has been measured several times by 12 different operating conditions, this means by different speeds, load moments and load forces. The current signals are measured with a current probe and an oscilloscope on two phases.
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Bator M. Dataset for Sensorless Drive Diagnosis. UCI Machine Learning Repository; 2013. doi:10.24432/C5VP5F
Bator, M. (2013). Dataset for Sensorless Drive Diagnosis. UCI Machine Learning Repository. https://doi.org/10.24432/C5VP5F
Bator M (2013) Dataset for Sensorless Drive Diagnosis. UCI Machine Learning Repository.
Bator, Martyna. Dataset for Sensorless Drive Diagnosis. UCI Machine Learning Repository, 2013. https://doi.org/10.24432/C5VP5F.
Bator, Martyna. 2013. Dataset for Sensorless Drive Diagnosis. UCI Machine Learning Repository. doi:10.24432/C5VP5F, .
Bator, Martyna: Dataset for Sensorless Drive Diagnosis : UCI Machine Learning Repository, 2013
M. Bator, Dataset for Sensorless Drive Diagnosis, UCI Machine Learning Repository, 2013.
M. Bator, Dataset for Sensorless Drive Diagnosis. UCI Machine Learning Repository, 2013. doi: 10.24432/C5VP5F.
Bator, Martyna. Dataset for Sensorless Drive Diagnosis. UCI Machine Learning Repository, 2013, https://doi.org/10.24432/C5VP5F.
Bator, Martyna: Dataset for Sensorless Drive Diagnosis, o. O. 2013.
Bator M. Dataset for Sensorless Drive Diagnosis. UCI Machine Learning Repository; 2013.
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