A comparative study on classification of features by SVM and PSVM extracted using Morlet wavelet for fault diagnosis of spur bevel gear box
A comparative study on classification of features by SVM and PSVM extracted using Morlet wavelet for fault diagnosis of spur bevel gear box
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DOI：
10.1016/j.eswa.2007.08.026
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发表时间：
2008-10
期刊：
影响因子：
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通讯作者：
N. Saravanan;V.N.S. Kumar Siddabattuni;K. I. Ramachandran
中科院分区：
文献类型：
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作者：
N. Saravanan;V.N.S. Kumar Siddabattuni;K. I. Ramachandran
使用在某个方便的位置测量的机器振动信号，并进一步处理以揭示这些信号的重要性，可以监测操作机器中不可接近的齿轮的状况。本文讨论了基于小波特征的故障诊断支持向量机（SVM）和近似支持向量机（PSVM）的有效性。采用J48算法对Morlet小波系数的统计特征向量进行分类，并将其中的优势特征作为SVM和PSVM的训练和测试输入，比较了它们对锥齿轮箱故障分类的相对效率。
The condition of an inaccessible gear in an operating machine can be monitored using the vibration signal of the machine measured at some convenient location and further processed to unravel the significance of these signals. This paper deals with the effectiveness of wavelet-based features for fault diagnosis using support vector machines (SVM) and proximal support vector machines (PSVM). The statistical feature vectors from Morlet wavelet coefficients are classified using J48 algorithm and the predominant features were fed as input for training and testing SVM and PSVM and their relative efficiency in classifying the faults in the bevel gear box was compared.