Mechanical fault diagnosis based on redundant second generation wavelet packet transform, neighborhood rough set and support vector machine
Mechanical fault diagnosis based on redundant second generation wavelet packet transform, neighborhood rough set and support vector machine
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DOI：
10.1016/j.ymssp.2011.10.016
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发表时间：
2012-04-01
影响因子：
8.4
通讯作者：
Liu, Xiaohang
中科院分区：
文献类型：
--
作者：
Li, Ning;Zhou, Rui;Liu, Xiaohang
本文研究了冗余第二代小波包变换（RSGWPT）、邻域粗糙集（NRS）和支持向量机（SVM）在故障检测、属性约简和模式分类中的应用。在此基础上，提出了一种基于RSGWFT、NRS和SVM的机械故障诊断新方法，利用RSGWFT从小波包系数的统计特性中提取故障特征参数构成特征向量，然后通过NRS方法进行属性约简得到关键特征，最后将这些关键特征输入到SVM中完成故障模式分类。所提方法对变速箱和汽油机配气机构故障诊断的实验结果表明，该方法能够提取故障特征，具有较好的分类能力，同时在保证分类精度的情况下减少了大量冗余特征，相应提高了分类器效率，取得了较好的分类性能。 (C) 2011 Elsevier Ltd. 保留所有权利。
This paper investigates the application of the redundant second generation wavelet package transform (RSGWPT), neighborhood rough set (NRS) and support vector machine (SVM) on faulty detection, attribute reduction and pattern classification. On this basis, a novel method for mechanical faulty diagnosis based on RSGWFT, NRS and SVM is presented, which utilizes the RSGWFT to extract faulty feature parameters from the statistical characteristics of wavelet package coefficients to constitute feature vectors, and then makes the attribute reduction by NRS method to obtain the key features, lastly these key features are input into SVM to accomplish faulty pattern classification. The experimental results of the proposed method to fault diagnosis of the gearbox and gasoline engine valve trains show that this method can extract the faulty features, which have better classification ability and at the same time reduce a lot of redundant features in case of assuring the classification accuracy, accordingly improve the classifier efficiency and achieve a better classification performance. (C) 2011 Elsevier Ltd. All rights reserved.