Abstract
Bearing is one of the key components of a rotating machine. Hence, monitoring health condition of the bearing is of paramount importace. This paper develops a novel particle swarm optimization (PSO)-least squares wavelet support vector machine (PSO-LSWSVM) classifier, which is designed based on a combination between a PSO, a least squares procedure, and a new wavelet kernel function-based support vector machine (SVM), for bearing fault diagnosis. In this work, bearing fault classification is transformed into a pattern recognition problem, which consists of three stages of data processing. Firstly, a rich information dataset is built by extracting the features from the signals, which are decomposed by the nonlocal means (NLM) and empirical mode decomposition (EMD). Secondly, a minimum-redundancy maximum-relevance (mRMR) method is employed to determine a subset of feature that can provide an optimal performance. Thirdly, a novel classifier, namely LSWSVM, is proposed with the aid of a PSO, to provide higher classification accuracy. The key innovative science of this work is to propropose a new classifier with the aid of an new wavelet kernel type to increase the classification precision of bearing fault diagnosis. The merit features of the proposed approach are demonstrated based on a benchmark bearing dataset and a comprehensive comparison procedure.
Highlights
Since bearing is a crucial component in the machine, its failure will hugely affect to the disruption of the machine
A new methodology for bearing fault diagnosis is developed by combining between feature extration based on a nonlocal means (NLM)-empirical mode decomposition (EMD) method, a feature selection based on a minimum-redundancy maximum-relevance (mRMR) and a new particle swarm optimization (PSO)-least squares wavelet support vector machine (LSWSVM) classifier
To improve the generalization performance of the support vector machine (SVM), a novel PSO-LSWSVM classifier, which combines between a least squares procedure, a new wavelet kernel function and the PSO, is proposed
Summary
Since bearing is a crucial component in the machine, its failure will hugely affect to the disruption of the machine. Due to its high performance classification and less requirement on sample data input, the support vector machine (SVM) proposed by Cortes and Vapnik [7] has been successfully applied to signal processing [8], regression analysis [9], pattern recognition [10], and bearing fault diagnosis [11]. Due to the merits of the LSSVM classifier and the approximation capability of the wavelet kernel, a new least squares wavelet support vector machine (LSWSVM) is proposed first time in this paper to improve both computational efficiency and classification accuracy. A new methodology for bearing fault diagnosis is developed by combining between feature extration based on a NLM-EMD method, a feature selection based on a mRMR and a new PSO-LSWSVM classifier. To improve the generalization performance of the SVM, a novel PSO-LSWSVM classifier, which combines between a least squares procedure, a new wavelet kernel function and the PSO, is proposed
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