원문정보
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
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