A new fault diagnosis method of rotating machinery
A new fault diagnosis method of rotating machinery
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DOI：
10.1155/2008/203621
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发表时间：
2008-01-01
影响因子：
1.6
通讯作者：
Ma, Chih-Kao
中科院分区：
文献类型：
--
作者：
Chen, Chih-Hao;Shyu, Rong-Juin;Ma, Chih-Kao
提出了一种基于小波包分形技术和径向基函数神经网络的旋转机械故障诊断方法。本研究所考虑的旋转机械故障包括不平衡、不对中、松动以及不平衡与不对中组合的情况。当这种故障发生时，它们通常会引起机器的非平稳振动。在对振动信号进行测量后，对测量信号进行小波包变换。提取各频段的分形维数，并采用计盒维数来刻画振动信号的故障特征。故障模式，然后分类的径向基函数神经网络。实验结果表明，该方法能有效地检测和识别旋转机械的各种故障。
This paper presents a new fault diagnosis procedure for rotating machinery using the wavelet packets-fractal technology and a radial basis function neural network. The faults of rotating machinery considered in this study include imbalance, misalignment, looseness and imbalance combined with misalignment conditions. When such faults occur, they usually induce non-stationary vibrations to the machine. After measuring the vibration signals, the wavelet packets transform is applied to these signals. The fractal dimension of each frequency bands is extracted and the box counting dimension is used to depict the failure characteristics of the vibration signals. The failure modes are then classified by a radial basis function neural network. An experimental study was performed to evaluate the proposed method and the results show that the method can effectively detect and recognize different kinds of faults of rotating machinery.