Spur bevel gearbox fault diagnosis using wavelet packet transform and rough set theory
Spur bevel gearbox fault diagnosis using wavelet packet transform and rough set theory
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基于小波包变换和粗糙集理论的直齿锥齿轮箱故障诊断
DOI：
10.1007/s10845-015-1174-x
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发表时间：
2015-12
期刊：
影响因子：
--
通讯作者：
Xuezeng Zhao
中科院分区：
文献类型：
--
作者：
Wentao Huang;Fanzhao Kong;Xuezeng Zhao
齿轮箱是工业传动中的重要部件，为工业生产提供安全可靠的运行。采用小波包变换（WPT）分析方法提取齿轮箱振动信号中的故障特征。将提取的WPT特征作为粗糙集（RS）的输入进行属性约简，然后结合遗传算法得到全局最优属性约简结果。利用属性约简后得到的故障特征生成决策规则。将未知的齿轮状态信号属性作为输入，与生成的决策规则进行匹配，用于故障诊断。齿轮箱振动信号包含大量的齿轮状态信息；WPT具有从振动信号中提取属性信息的敏锐的部分锁定能力。但是，小波变换频率混叠会导致杂散频率分量的产生，影响齿轮故障诊断。在本文中，我们引入了一种改进的小波变换来消除频率混叠，从而提高故障诊断的准确性。研究了用小波包进行特征提取，用RS进行分类；结果表明，该方法能够准确、可靠地检测齿轮箱的故障模式。
The gearbox is an important component in industrial drives, providing safe and reliable operation for industrial production. Wavelet packet transform (WPT) analysis was used to extract fault features in the vibration signals generated by a gearbox. The extracted features from the WPT were used as input in a rough set (RS) for attribute reduction and then combined with a genetic algorithm to obtain global optimal attribute reduction results. The fault features gained after the attribute reductions were used to generate decision rules. The unknown gear status signal attributes were used as input to match the generated decision rules for fault diagnosis purposes. Gearbox vibration signals contain a significant amount of gear status information; a WPT has an acute portion-locked ability to extract attribute information from the vibration signals. However, WPT frequency aliasing would lead to the generation of spurious frequency components, affecting gear fault diagnosis. In this paper, we introduce an improved WPT to eliminate frequency aliasing, thus improving the accuracy of fault diagnosis. This paper studies the use of wavelet packet for feature extraction and the RS for classification; the results demonstrate that this method can accurately and reliably detect failure modes in a gearbox.
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发表时间：
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期刊：
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