Fault diagnosis of spur bevel gear box using artificial neural network (ANN), and proximal support vector machine (PSVM)
Fault diagnosis of spur bevel gear box using artificial neural network (ANN), and proximal support vector machine (PSVM)
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DOI：
10.1016/j.asoc.2009.08.006
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
N. Saravanan;Vns Siddabattuni;K. I. Ramachandran
中科院分区：
文献类型：
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作者：
N. Saravanan;Vns Siddabattuni;K. I. Ramachandran
从机械旋转部件提取的振动信号携带了大量有关操作机器状况的信息。对在机器方便位置测量的这些原始振动特征进行进一步处理，可以揭示所研究的部件或组件的状况。本文讨论了使用人工神经网络 (ANN) 和近端支持向量机 (PSVM) 进行齿轮箱故障诊断的基于小波特征的有效性。使用 J48 算法对来自 Morlet 小波系数的统计特征向量进行分类，并将主要特征作为训练和测试 ANN 和 PSVM 的输入，并比较它们在锥齿轮箱故障分类中的相对效率。
Vibration signals extracted from rotating parts of machineries carries lot many information with in them about the condition of the operating machine. Further processing of these raw vibration signatures measured at a convenient location of the machine unravels the condition of the component or assembly under study. This paper deals with the effectiveness of wavelet-based features for fault diagnosis of a gear box using artificial neural network (ANN) 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 ANN and PSVM and their relative efficiency in classifying the faults in the bevel gear box was compared.