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基于小波包能量和调制信号双谱边带估计的齿轮磨损监测

Gear Wear Monitoring based on Wavelet Packet Energy and Modulation Signal Bispectrum Sideband Estimator

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【作者】 温新宇张瑞亮谷丰收刘普施延栋

【Author】 Wen Xinyu;Zhang Ruiliang;Gu Fengshou;Liu Pu;Shi Yandong;Gear Research Institute,Taiyuan University of Technology;School of Computing and Engineering,University of Huddersfield;

【通讯作者】 张瑞亮;

【机构】 太原理工大学齿轮研究所哈德斯菲尔德大学计算与工程学院

【摘要】 为提高调制信号双谱边带估计的分析效果和鲁棒性,提出了小波包能量和调制信号双谱边带估计相结合的磨损特征提取方法。首先,使用小波包变换将信号分解为多个频段,计算每个频段的小波包能量,选取小波包能量较高的频段进行重构,以达到优选分析频段和降低计算量的目的;然后,对重构信号进行调制信号双谱边带估计以提取齿面磨损特征,并通过合理构建指标实现齿面磨损损伤状态的在线监测。仿真与试验结果表明,所提出方法具有更强的鲁棒性,能够更准确地监测齿面磨损的发展过程,评估磨损损伤程度。

【Abstract】 In order to improve the analytical result and robustness of modulation signal bispectrum based sideband estimator(MSB-SE),a method for extracting wear characteristics combining wavelet packet energy(WPE) and MSB-SE is proposed.Firstly,the wavelet packet transform is used to decompose the signal into multiple frequency bands,and the WPE of each frequency band is calculated.The frequency band with higher energy is selected for reconstruction to optimize the analysis frequency band and reduce the calculation amount.Then,the MSB-SE is carried out to extract the characteristics of tooth surface wear,and the online monitoring of tooth surface wear damage state is realized by using a reasonable index.Simulation and experimental results show that the proposed method is more robust,and can more accurately monitor tooth surface wear and evaluate the degree of wear damage.

【基金】 山西省自然科学基金(201801D121181)
  • 【文献出处】 机械传动 ,Journal of Mechanical Transmission , 编辑部邮箱 ,2021年02期
  • 【分类号】TH132.41
  • 【被引频次】4
  • 【下载频次】187
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