DocumentCode
168385
Title
Effective Motor´s Quality Types Determination on Motor´s Current Waveforms Using the Euclidean Distance Measurement Method
Author
Liuh-Chii Lin ; Yun-Chi Yeh ; You-Wei Song
Author_Institution
Dept. of Electron. Eng., Chien Hsin Univ. of Sci. & Technol., Jhongli, Taiwan
fYear
2014
fDate
10-12 June 2014
Firstpage
1225
Lastpage
1228
Abstract
This study proposes a simple and effective method, termed Euclidean Distance Measurement (EDM) method, to analyze motor´s current waveforms for effectively determining the motor´s quality types. This method is easily performed and does not require complex mathematic computations. It can recognize good or defective motors as well as defect types in less than 0.5 sec, and the maximum memory requirement is only about 10 MB for each motor´s quality types with 16 bit sampling points. The EDM is described as the following two subsections: computing the mean vectors for each quality type and the motor´s quality type determination. In the experiment, the total classification accuracy was approximately 99.68%.
Keywords
DC motors; quality control; DC motor; EDM; Euclidean distance measurement method; motor current waveforms; motor quality types determination; Accuracy; Adaptation models; Current measurement; Euclidean distance; Heart beat; Support vector machine classification; Vectors; DC motor; Euclidean Distance Measurement; motor´s current waveform; qualitative features selection; total classification accuracy (TCA);
fLanguage
English
Publisher
ieee
Conference_Titel
Computer, Consumer and Control (IS3C), 2014 International Symposium on
Conference_Location
Taichung
Type
conf
DOI
10.1109/IS3C.2014.317
Filename
6846109
Link To Document