Earticle

초록

영어

This paper presents three texture feature extraction techniques: gray level co-occurrence matrix (GLCM), Gabor filter, and global neighborhood structure (GNS) map, for the fault diagnosis of induction motors. The texture of twodimensional (2D) gray level images is converted from acoustic emission (AE) fault signals and used for feature extraction of the fault signals. The extracted texture features are used as inputs to a multi-class support vector machine (MCSVM) to classify each fault. The Gaussian radial basis function kernel is used with MCSVM to handle non-linear fault features of acoustic emission (AE) signals. Experimental results with one-second AE signals sampled at 1 MHz showed that the GLCM-based feature extraction method outperformed the Gabor filter and the GNS map in terms of classification accuracy because of its ability to capture the spatial dependence of gray-level texture values.

목차

Abstract
 I. INTRODUCTION
 II. 2D FAULT DIAGNOSIS METHOD
  A. Data Conversion
  B. Text Feature Extraction Methods
  C. Fault Classification
 III. EXPERIMENTAL RESULTS AND ANALYSIS
  A. Text Rig and Fault Signals of Induction Motors
  B. Experimental Results and Analysis
 IV. CONCLUSIONS
 REFERENCES

키워드

저자

• Jia Uddin [ Dept. of Electrical, Electronics, and Computer Engineering University of Ulsan Ulsan, South Korea ]
• Rashedul Islam [ Dept. of Electrical, Electronics, and Computer Engineering University of Ulsan Ulsan, South Korea ]
• Jong-Myon Kim [ Dept. of Electrical, Electronics, and Computer Engineering University of Ulsan Ulsan, South Korea ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  JoC [Journal of Convergence]

• 간기

  계간

• pISSN

  2093-7741

• eISSN

  2093-775X

• 수록기간

  2010~2015

• 십진분류

  KDC 004 DDC 004