کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
727653 892776 2011 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Support vector machine with genetic algorithm for machinery fault diagnosis of high voltage circuit breaker
موضوعات مرتبط
مهندسی و علوم پایه سایر رشته های مهندسی کنترل و سیستم های مهندسی
پیش نمایش صفحه اول مقاله
Support vector machine with genetic algorithm for machinery fault diagnosis of high voltage circuit breaker
چکیده انگلیسی

Based on empirical mode decomposition (EMD) method and support vector machine (SVM), a new method for the fault diagnosis of high voltage circuit breaker (CB) is proposed. The feature extraction method based on improved EMD energy entropy is detailedly analyzed and SVM is employed as a classifier. Radial basis function (RBF) is adopted as the kernel function of SVM and its kernel parameter γ and penalty parameter C must be carefully predetermined in establishing an efficient SVM model. Therefore, the purpose of this study is to develop a genetic algorithm-based SVM (GA-SVM) model that can determine the optimal parameters of SVM with the highest accuracy and generalization ability. The classification accuracy of this GA-SVM approach is tried by real dataset and compared with the SVM, which has randomly selected kernel function parameters. The experimental results indicate that the classification accuracy of this GA-SVM approach is more superior than that of the artificial neural network and the SVM which has constant and manually extracted parameters.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Measurement - Volume 44, Issue 6, July 2011, Pages 1018–1027
نویسندگان
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