Dataset for Sensorless Drive Diagnosis
Donated on 2/23/2015
Features are extracted from motor current. The motor has intact and defective components. This results in 11 different classes with different conditions.
Dataset Characteristics
Multivariate
Subject Area
Computer Science
Associated Tasks
Classification
Feature Type
Real
# Instances
58509
# Features
-
Dataset Information
Additional 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.
Has Missing Values?
No
Variables Table
| Variable Name | Role | Type | Description | Units | Missing Values |
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Additional Variable 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.
Dataset Files
| File | Size |
|---|---|
| Sensorless_drive_diagnosis.txt | 24.4 MB |
Reviews
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset dataset_for_sensorless_drive_diagnosis = fetch_ucirepo(id=325) # data (as pandas dataframes) X = dataset_for_sensorless_drive_diagnosis.data.features y = dataset_for_sensorless_drive_diagnosis.data.targets # metadata print(dataset_for_sensorless_drive_diagnosis.metadata) # variable information print(dataset_for_sensorless_drive_diagnosis.variables)
Bator, M. (2013). Dataset for Sensorless Drive Diagnosis [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5VP5F.
Creators
Martyna Bator
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.