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1.河北建筑工程学院 机械工程学院,张家口 075000
2.河北省装配式建筑预制构件产线智能化技术创新中心,张家口 075000
3.天津忠旺铝业有限公司,天津 301729
吴东昊,男,1995年生,河北怀安人,硕士,实验师;主要研究方向为信号处理等;wdh2147@hebiace.edu.cn。
收稿:2024-12-01,
纸质出版:2026-03-15
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吴东昊,刘丽娟,马立勇,等. 基于ITD-WPT的齿轮裂纹冲击信号降噪方法[J]. 机械传动,2026,50(3):179-186.
WU Donghao,LIU Lijuan,MA Liyong,et al. Noise reduction method for gear crack impact signals based on ITD-WPT[J]. Journal of Mechanical Transmission,2026,50(3):179-186.
吴东昊,刘丽娟,马立勇,等. 基于ITD-WPT的齿轮裂纹冲击信号降噪方法[J]. 机械传动,2026,50(3):179-186. DOI: 10.16578/j.issn.1004.2539.2026.03.020.
WU Donghao,LIU Lijuan,MA Liyong,et al. Noise reduction method for gear crack impact signals based on ITD-WPT[J]. Journal of Mechanical Transmission,2026,50(3):179-186. DOI: 10.16578/j.issn.1004.2539.2026.03.020.
目的
2
针对利用传统降噪方法处理齿轮裂纹冲击信号时降噪效果欠佳、关键特征保留不足的问题,提出一种融合固有时间尺度分解(Intrinsic Time-scale Decomposition
ITD)与小波包变换(Wavelet Packet Transform
WPT)的降噪方法(ITD-WPT)。
方法
2
首先,采用ITD将含噪信号分解为若干固有旋转(Proper Rotation
PR)分量,通过识别异常PR分量分离含噪高频分量,为精准降噪奠定基础;其次,利用WPT对含噪高频分量进行多尺度分解,通过精细阈值处理提取小波包系数,实现噪声精准抑制;然后,重构小波包去噪分量,结合无噪低频分量,采用ITD进行信号重构,获得高质量降噪信号;最后,将该方法应用于仿真裂纹齿轮信号与试验断齿信号,与滑动平均、经验模态分解(Empirical Mode Decomposition
EMD)、小波降噪等经典方法进行对比验证。
结果
2
结果表明,该方法降噪效果显著优于对比方法:噪声方差为50时,信噪比达9.490 dB,相关系数为0.944;方差为100时,信噪比为4.012 dB,相关系数为0.838,且能有效保留信号冲击特征。研究可为齿轮裂纹冲击信号降噪提供参考。
Objective
2
Aiming at the problems of poor denoising effect and insufficient retention of key features when traditional denoising methods process gear crack impact signals
a denoising method (ITD-WPT) integrating intrinsic time-scale decomposition (IDT) and wavelet packet transform (WPT) was proposed to achieve efficient denoising and accurate feature retention of gear crack impact signals.
Methods
2
Firstly
the noisy signal was decomposed into several proper rotation (PR) components by ITD
and high-frequency components containing noise were separated by identifying abnormal PR components to lay a foundation for precise denoising. Secondly
WPT was used for multi-scale decomposition of the noisy high-frequency components
and wavelet packet coefficients were extracted through refined threshold processing to achieve accurate noise suppression. Then
the wavelet packet denoised components were reconstructed
and signal reconstruction was performed using ITD combined with noise-free low-frequency components to obtain high-quality denoised signals. Finally
the method was applied to simulated cracked gear signals and test broken tooth signals
and comparative verification was conducted with classical methods such as moving average
empirical mode decomposition (EMD) and wavelet denoising.
Results
2
The results show that the denoising effect of this method is significantly superior to the comparative methods: when the noise variance is 50
the signal-to-noise ratio (SNR) reaches 9.490 dB and the correlation coefficient is 0.944; when the variance is 100
the SNR is 4.012 dB and the correlation coefficient is 0.838. Moreover
it could effectively retain the impact characteristics of signals
providing a reference for the denoising of gear crack impact signals.
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