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

Xinyu Wen; Ruiliang Zhang; Fengshou Gu; Pu Liu; Yandong Shi

doi:10.16578/j.issn.1004.2539.2021.02.018

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

Test·Analysis | 更新时间：2022-11-10

- Gear Wear Monitoring based on Wavelet Packet Energy and Modulation Signal Bispectrum Sideband Estimator
- Journal of Mechanical Transmission Vol. 45, Issue 2, Pages: 117-123(2021)
- 作者机构：
  
  1.太原理工大学齿轮研究所，山西太原 030024
  2.哈德斯菲尔德大学计算与工程学院，英国哈德斯菲尔德 HD1 3DH
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2021.02.018
  CLC：
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温新宇,张瑞亮,谷丰收等.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[J].机械传动,2021,45(02):117-123.

Wen Xinyu,Zhang Ruiliang,Gu Fengshou,et al.Gear Wear Monitoring based on Wavelet Packet Energy and Modulation Signal Bispectrum Sideband Estimator[J].Journal of Mechanical Transmission,2021,45(02):117-123.
温新宇,张瑞亮,谷丰收等.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[J].机械传动,2021,45(02):117-123. DOI： 10.16578/j.issn.1004.2539.2021.02.018.

Wen Xinyu,Zhang Ruiliang,Gu Fengshou,et al.Gear Wear Monitoring based on Wavelet Packet Energy and Modulation Signal Bispectrum Sideband Estimator[J].Journal of Mechanical Transmission,2021,45(02):117-123. DOI： 10.16578/j.issn.1004.2539.2021.02.018.

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.

关键词

齿轮传动磨损监测调制信号双谱小波包变换

Keywords

Gear transmissionWear monitoringModulation signal bispectrumWavelet packet transformation

references

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