• 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