Zhou Xiaolong, Wang Xiangkun, Wang Haotian, Xing Zhongyuan, Yang Zhilun, Cao Linlin
Mechanical Engineering College, Beihua University, Jilin City 132021, China.
Sensors (Basel). 2023 Aug 3;23(15):6904. doi: 10.3390/s23156904.
The vibration signals from rotating machinery are constantly mixed with other noises during the acquisition process, which has a negative impact on the accuracy of signal feature extraction. For vibration signals from rotating machinery, the conventional linear filtering-based denoising method is ineffective. To address this issue, this paper suggests an enhanced signal denoising method based on maximum overlap discrete wavelet packet transform (MODWPT) and variational mode decomposition (VMD). VMD decomposes the vibration signal of rotating machinery to produce a set of intrinsic mode functions (IMFs). By computing the composite weighted entropy (CWE), the phantom IMF component is then removed. In the end, the sensitive component is obtained by computing the value of the degree of difference (DID) after the high-frequency noise component has been decomposed through MODWPT. The denoised signal reconstructs the signal's intrinsic characteristics as well as the denoised high-frequency IMF component. This technique was used to analyze the simulated and real-world signals of gear faults and it was compared to wavelet threshold denoising (WTD), empirical mode decomposition reconstruction denoising (EMD-RD), and ensemble empirical mode decomposition wavelet threshold denoising (EEMD-WTD). The outcomes demonstrate that this method can accurately extract the signal feature information while filtering out the noise components in the signal.
旋转机械的振动信号在采集过程中不断与其他噪声混合，这对信号特征提取的准确性有负面影响。对于旋转机械的振动信号，传统的基于线性滤波的去噪方法无效。为了解决这个问题，本文提出了一种基于最大重叠离散小波包变换（MODWPT）和变分模态分解（VMD）的增强型信号去噪方法。VMD对旋转机械的振动信号进行分解，产生一组固有模态函数（IMF）。通过计算复合加权熵（CWE），然后去除幻像IMF分量。最后，在通过MODWPT对高频噪声分量进行分解后，通过计算差异度（DID）的值来获得敏感分量。去噪后的信号重建了信号的固有特征以及去噪后的高频IMF分量。该技术用于分析齿轮故障的模拟信号和实际信号，并与小波阈值去噪（WTD）、经验模态分解重建去噪（EMD-RD）和总体经验模态分解小波阈值去噪（EEMD-WTD）进行了比较。结果表明，该方法能够准确提取信号特征信息，同时滤除信号中的噪声分量。
