Effective Motor’s Quality Types Determination on Motor’s Current Waveforms Using the Euclidean Distance Measurement Method

• Main Authors: You-Wei Song, 宋有維
• Other Authors: Yun-Chi Yeh
• Format: Others
• Language: zh-TW
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/33303572174499916078

碩士 === 健行科技大學 === 電子工程系碩士班 === 103 === 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. Using EDM consists of three major stages: (i) the preprocessing stage for enlarging motor’s current waveforms’ amplitude and eliminating noises; (ii) the qualitative features stage for qualitative feature selection of a motor’s current waveform; and (iii) the classification stage for determining motor’s quality types. 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: (1)computing the mean vectors for each quality type and (2)the motor’s quality type determination. In the experiment, the total classification accuracy was approximately 99.68%.