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SensorlessDriveDiagnosis

SensorlessDriveDiagnosis

in_preparation ARFF Publicly available Visibility: public Uploaded 16-02-2016 by Hilda Fabiola Bernard
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Author: Martyna Bator (University of Applied Sciences","Ostwestfalen-Lippe","martyna.bator '@' hs-owl.de) Source: UCI Please cite: Please refer to the Machine Learning Repository's citation policy Source: Owner of database: Martyna Bator (University of Applied Sciences, Ostwestfalen-Lippe, martyna.bator '@' hs-owl.de) Donor of database: Martyna Bator (University of Applied Sciences, Ostwestfalen-Lippe, martyna.bator '@' hs-owl.de) Data Set Information: 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. Attribute Information: The Empirical Mode Decomposition (EMD) was used to generate a new database for the generation of features. The first three intrinsic mode functions (IMF) of the two phase currents and their residuals (RES) were used and broken down into sub-sequences. For each of this sub-sequences, the statistical features mean, standard deviation, skewness and kurtosis were calculated. Relevant Papers: PASCHKE, Fabian ; BAYER, Christian ; BATOR, Martyna ; MÖNKS, Uwe ; DICKS, Alexander ; ENGE-ROSENBLATT, Olaf ; LOHWEG, Volker: Sensorlose Zustandsüberwachung an Synchronmotoren, Bd. 46. In: HOFFMANN, Frank; HÜLLERMEIER, Eyke (Hrsg.): Proceedings 23. Workshop Computational Intelligence. Karlsruhe : KIT Scientific Publishing, 2013 (Schriftenreihe des Instituts für Angewandte Informatik - Automatisierungstechnik am Karlsruher Institut für Technologie, 46), S. 211-225 Citation Request: Please refer to the Machine Learning Repository's citation policy

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11 tasks

0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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