A multidimensional hybrid intelligent method for gear fault diagnosis
A multidimensional hybrid intelligent method for gear fault diagnosis
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DOI：
10.1016/j.eswa.2009.06.060
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发表时间：
2010-03-01
影响因子：
8.5
通讯作者：
Zi, Yanyang
中科院分区：
文献类型：
--
作者：
Lei, Yaguo;Zuo, Ming J.;Zi, Yanyang
识别齿轮损坏类别。特别是早期断层和复合断层。提出了一种新的多维混合智能诊断方法，能够自动识别齿轮的不同损伤类别和损伤程度。在这种方法中，进行了Hilbert变换。对齿轮振动信号进行小波包变换和经验模态分解，提取附加的故障特征信息。生成包括时域、频域和时频域特征的多维特征集以揭示齿轮健康状况。基于遗传算法的多分类器是将多种分类算法和输入特征与遗传算法相结合。由于使用了多维特征和多分类器的组合。利用所提出的方法，期望得到更准确的诊断结果。进行了不同齿轮损伤类别和损伤程度的试验。并在不同负载和电机转速下采集振动信号。将该方法应用于采集到的齿轮损伤信号中，识别齿轮的损伤类别和损伤程度。诊断结果表明，该方法能够可靠地识别出单一的损伤模式。联合损伤模式和损伤等级（C）2009爱思唯尔有限公司保留所有权利。
Identifying gear damage categories. especially for early faults and combined faults. is a challenging task in gear fault diagnosis This paper proposes a new multidimensional hybrid intelligent diagnosis method to identify different categories and levels of gear damage automatically. In this method, Hilbert transform. wavelet packet transform (WPT) and empirical mode decomposition (EMD) are performed on gear vibration signals to extract additional fault characteristic information Then. multidimensional feature sets including time-domain, frequency-domain and time-frequency-domain features are generated to reveal gear health conditions. Multiple classifiers based oil several classification algorithms and input features are combined with genetic algorithm (GA). Because of the use of multidimensional features and the combination of multiple classifiers. more accurate diagnosis results are expected with the proposed method. Experiments with different gear damage categories and damage levels were conducted. and the vibration signals were captured under different loads and motor speeds. The proposed method is applied to the collected signals to identify the gear damage categories and damage levels. The diagnosis results show it can reliably recognize single damage modes. combined damage modes, and damage levels (C) 2009 Elsevier Ltd All rights reserved.