Center for Machine Learning and Intelligent Systems
About  Citation Policy  Donate a Data Set  Contact


Repository Web            Google
View ALL Data Sets

Dataset for Sensorless Drive Diagnosis Data Set
Download: Data Folder, Data Set Description

Abstract: Features are extracted from motor current. The motor has intact and defective components. This results in 11 different classes with different conditions.

Data Set Characteristics:  

Multivariate

Number of Instances:

58509

Area:

Computer

Attribute Characteristics:

Real

Number of Attributes:

49

Date Donated

2015-02-24

Associated Tasks:

Classification

Missing Values?

N/A

Number of Web Hits:

29164


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


Supported By:

 In Collaboration With:

About  ||  Citation Policy  ||  Donation Policy  ||  Contact  ||  CML