Abstract: To improve the noise reduction accuracy of reciprocating friction vibration signals of piston ring friction pairs in marine diesel engines, this paper proposes a collaborative noise reduction method that integrates complementary ensemble empirical mode decomposition (CEEMD) and an adaptive correlation coefficient screening mechanism. Simulated vibration signals were obtained using the BRUKER UMT friction and wear testing machine to construct an experimental dataset. The original signals were subjected to multi-scale decomposition using CEEMD, and effective intrinsic modal components were screened using an adaptive correlation coefficient threshold. The dominant noise component was removed to achieve signal reconstruction. MATLAB software was applied to implement the noise reduction processing. Three types of indicators—signal-to-noise ratio (SNR), normalized cross-correlation coefficient (NCC), and mean square error (MSE)—were used for quantitative evaluation. Multi-scale permutation entropy (MPE) theory was also innovatively introduced to verify the dynamic characteristics of the denoised signal. The experimental results show that the CEEMD adaptive correlation coefficient screening method has significantly improved noise reduction performance compared to other methods. The correlation coefficients between the multi-scale permutation entropy image of the stripped noise signal and the overall laboratory noise signal image are all above 0.8, thus proving the accuracy of effective signal denoising.

Key words: friction vibration signals, noise reduction processing, adaptive correlation coefficient, complementary ensemble empirical mode decomposition (CEEMD), multi-scale permutation entropy (MPE)

摘要： 为提高船舶柴油机活塞环摩擦副往复摩擦振动信号的降噪精度,该文提出一种融合互补集合经验模态分解（complementary ensemble empirical mode decomposition,CEEMD）与自适应相关系数筛选机制的协同降噪方法。首先利用BRUKER UMT摩擦磨损试验机获取模拟振动信号以构建实验数据集,基于CEEMD对原始信号进行多尺度分解,结合自适应相关系数阈值筛选有效本征模态分量,剔除噪声主导分量以实现信号重构;后续应用MATLAB软件实施降噪处理,以信噪比、归一化互相关系数及均方误差这3类指标开展量化评估,并创新性引入多尺度排列熵理论验证降噪信号的动力学特征。实验结果表明：CEEMD自适应相关系数筛选方法的降噪效果显著优于其他方法,且剥离出的噪声信号多尺度排列熵图像与实验室噪声信号图像的整体相关系数均在0.8以上,从而证明了对有效信号降噪的准确性。

关键词: 摩擦振动信号, 降噪处理, 自适应相关系数, 互补集合经验模态分解, 多尺度排列熵