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首页> 外文期刊>Journal of Intelligent Manufacturing >Spur bevel gearbox fault diagnosis using wavelet packet transform and rough set theory
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Spur bevel gearbox fault diagnosis using wavelet packet transform and rough set theory

机译：使用小波包变换和粗糙集理论进行浇口斜面齿轮箱故障诊断

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摘要

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.
机译：变速箱是工业驱动器中的一个重要组成部分，为工业生产提供了安全可靠的操作。小波分组变换（WPT）分析用于提取由变速箱产生的振动信号中的故障特征。来自WPT的提取特征被用作粗糙集（RS）中的输入，以进行属性降低，然后与遗传算法组合以获得全局最优属性降低结果。属性缩减后获得的故障功能用于生成决策规则。未知的齿轮状态信号属性被用作输入以匹配生成的故障诊断目的的决策规则。变速箱振动信号包含大量的齿轮状态信息; WPT具有急性部分锁定的能力，可以从振动信号中提取属性信息。然而，WPT频率混叠将导致杂散频率分量的产生，影响齿轮故障诊断。在本文中，我们介绍了一种改进的WPT来消除频率叠种，从而提高了故障诊断的准确性。本文研究了使用小波包进行特征提取和用于分类的RS;结果表明，该方法可以准确可靠地检测变速箱中的故障模式。

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