{
    "data_id": "4536",
    "name": "SensorlessDriveDiagnosis",
    "exact_name": "SensorlessDriveDiagnosis",
    "version": 1,
    "version_label": null,
    "description": "**Author**: Martyna Bator (University of Applied Sciences\",\"Ostwestfalen-Lippe\",\"martyna.bator '@' hs-owl.de)  \n**Source**: UCI\n**Please cite**: Please refer to the Machine Learning Repository's citation policy  \n\nSource:\n\nOwner of database: Martyna Bator (University of Applied Sciences, Ostwestfalen-Lippe, martyna.bator '@' hs-owl.de) \nDonor of database: Martyna Bator (University of Applied Sciences, Ostwestfalen-Lippe, martyna.bator '@' hs-owl.de)\n\n\nData Set Information:\n\nFeatures 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.\n\n\nAttribute Information:\n\nThe 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.\n\n\nRelevant Papers:\n\nPASCHKE, 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\n\n \n\nCitation Request:\n\nPlease refer to the Machine Learning Repository's citation policy",
    "format": "ARFF",
    "uploader": "Hilda Fabiola Bernard",
    "uploader_id": 874,
    "visibility": "public",
    "creator": "\"Martyna Bator (University of Applied Sciences\",\"Ostwestfalen-Lippe\",\"martyna.bator '@' hs-owl.de)\"",
    "contributor": null,
    "date": "2016-02-16 15:45:26",
    "update_comment": null,
    "last_update": "2016-02-16 15:45:26",
    "licence": "Public",
    "status": "in_preparation",
    "error_message": "XSD does not comply. XSD errors: xmlSAX2Characters: huge text node. XML does not correspond to XSD schema. Error xmlSAX2Characters: huge text node on line 25356 column 209. Error Element '{http:\/\/openml.org\/openml}error': [facet 'maxLength'] The value has a length of '9999990'; this exceeds the allowed maximum length of '1024'.\n on line 4 column 0. Error Element '{http:\/\/openml.org\/openml}error': 'Problem validating uploaded description file: XML does not correspond to XSD schema. Error Element '{http on line 4 column 0. ,XSD does not comply. XSD errors: xmlSAX2Characters: huge text node. XML does not correspond to XSD schema. Error xmlSAX2Characters: huge text node on line 25356 column 209. Error Element '{http:\/\/openml.org\/openml}error': [facet 'maxLength'] The value has a length of '9999990'; this exceeds the allowed maximum length of '1024'.\n on line 4 column 0. Error Element '{http:\/\/openml.org\/openml}error': 'Problem validating uploaded description file: XML does not correspond to XSD schema. Error Elem",
    "url": "https:\/\/www.openml.org\/data\/download\/1798108\/phpslQhBZ",
    "default_target_attribute": null,
    "row_id_attribute": null,
    "ignore_attribute": null,
    "runs": 0,
    "suggest": {
        "input": [
            "SensorlessDriveDiagnosis",
            "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 operat "
        ],
        "weight": 5
    },
    "qualities": [],
    "tags": [],
    "features": [],
    "nr_of_issues": 0,
    "nr_of_downvotes": 0,
    "nr_of_likes": 0,
    "nr_of_downloads": 0,
    "total_downloads": 0,
    "reach": 0,
    "reuse": 0,
    "impact_of_reuse": 0,
    "reach_of_reuse": 0,
    "impact": 0
}