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Wavelet support vector machine for induction machine fault diagnosis based on transient current signal

机译：基于暂态电流信号的小波支持向量机在异步电机故障诊断中的应用

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摘要

This paper presents establishing intelligent system for faults detection and classification of induction motor using wavelet support vector machine (W-SVM). Support vector machines (SVM) is well known as intelligent classifier with strong generalization ability. Application of nonlinear SVM using kernel function is widely used for multi-class classification procedure. In this paper, building kernel function using wavelet will be introduced and applied for SVM multi-class classifier. Moreover, the feature vectors for training classification routine are obtained from transient current signal that preprocessed by discrete wavelet transform. In this work, principal component analysis (PCA) and kernel PCA are performed to reduce the dimension of features and to extract the useful features for classification process. Hence, a relatively new intelligent faults detection and classification method called W-SVM is established. This method is used to induction motor for faults classification based on transient current signal. The results show that the performance of classification has high accuracy based on experimental work.
机译：本文提出了一种基于小波支持向量机（W-SVM）的异步电动机故障检测与分类智能系统。支持向量机（SVM）是众所周知的具有强大泛化能力的智能分类器。利用核函数的非线性支持向量机的应用广泛用于多类分类程序。本文将介绍使用小波构建核函数并将其应用于SVM多类分类器。此外，从经过离散小波变换预处理的瞬态电流信号中获得训练分类例程的特征向量。在这项工作中，执行主成分分析（PCA）和内核PCA以减小特征的维数并提取用于分类过程的有用特征。因此，建立了一种相对较新的智能故障检测与分类方法，称为W-SVM。该方法用于基于瞬时电流信号的异步电动机故障分类。结果表明，基于实验工作，分类性能具有较高的准确性。

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