【摘要】 针对滚动轴承在强背景噪声下造成故障特征不易识别的问题，提出一种以1.5维谱加权谐噪比（weighted harmonic-to-noiseratio，简称WHNR）为评价指标的自适应级联变分模态分解（cascadedvariationalmode decomposition，简称CVMD）特征增强方法。首先，基于不同故障特征频率计算1.5维谱下的最大WHNR来确定CVMD惩罚因子及分解层数；其次，利用1.5维谱对分解结果解调分析，进一步抑制噪声干扰，突出故障特征，最终提高特征辨识度，实现滚动轴承的故障特征增强；最后，通过仿真信号和滚动轴承故障实验，证明了该方法在强背景噪声情况下的优良去噪能力，能够增强微弱故障特征并抑制无关分量。更多还原

【Abstract】 Aiming at the problem that the fault characteristics of rolling bearings are difficult to identify under strong background noise, a feature enhancement method based on 1.5-dimensional spectral weighted harmonicto-noise ratio(WHNR) optimization cascaded variational mode decomposition(CVMD) is proposed. First, the penalty factor and the number of decomposition layers of CVMD are determined by calculating the maximum WHNR under the 1.5-dimensional spectrum by the eigenfrequencies of different faults. Than, the 1.5-dimensional spectrum is used for demodulating and analyzing the decomposition results to further suppress noise interference, improving feature recognition and enhancing the fault characteristics of rolling bearings. Finally, the excellent denoising ability of the method in the case of strong background noise, which can enhance weak fault features and suppress irrelevant components, are proved by simulation signal and experimental data analysis of rolling bearing failure.更多还原