کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
388607 660930 2007 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Application of nonlinear feature extraction and support vector machines for fault diagnosis of induction motors
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Application of nonlinear feature extraction and support vector machines for fault diagnosis of induction motors
چکیده انگلیسی

Recently, principal components analysis (PCA) and independent components analysis (ICA) was introduced for doing feature extraction. PCA and ICA linearly transform the original input into new uncorrelated and independent features space respectively. In this paper, the feasibility of using nonlinear feature extraction is studied and it is applied in support vector machines (SVMs) to classify the faults of induction motor. In nonlinear feature extraction, we employed the PCA and ICA procedure and adopted the kernel trick to nonlinearly map the data into a feature space. A strategy of multi-class SVM-based classification is applied to perform the faults diagnosis. The performance of classification process due to various feature extraction method and the choice of kernel function is presented and compared to show the excellent of classification process.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 33, Issue 1, July 2007, Pages 241–250
نویسندگان
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