基于1.5维谱WHNR优化CVMD的滚动轴承特征增强方法  

Feature Enhancement Method of Rolling Bearing by CVMD Optimization Based on 1.5⁃Dimensional Spectral Weighted Harmonic⁃Noise Ratio

作　　者：王晓昆 王航 邓强 刘诗文[4] 彭敏俊 徐仁义 WANG Xiaokun;WANG Hang;DENG Qiang;LIU Shiwen;PENG Minjun;XU Renyi(The National Defense Key Discipline Laboratory of Nuclear Safety and Simulation Technology,Harbin Engineering University Harbin,150001,China;Nuclear Safety and Advanced Nuclear Technology Laboratory,Harbin Engineering University Harbin,150001,China;Key Laboratory of Nuclear Power Plant Performance and Equipment,Harbin Engineering University Harbin,150001,China;China Nuclear Power Research and Design Institute,China National Nuclear Corporation Chengdu,610213,China)

机构地区：[1]哈尔滨工程大学核安全与仿真技术国防重点学科实验室,哈尔滨150001 [2]哈尔滨工程大学核安全与先进核能技术实验室,哈尔滨150001 [3]哈尔滨工程大学核动力装置性能与设备重点实验室,哈尔滨150001 [4]中国核工业集团中国核动力研究设计院,成都610213

出　　处：《振动.测试与诊断》2025年第1期80-87,201,共9页Journal of Vibration,Measurement & Diagnosis

基　　金：中国核工业集团公司“青年英才计划”资助项目(KY9020021007)。

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。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-dimen⁃sional spectrum is used for demodulating and analyzing the decomposition results to further suppress noise inter⁃ference,improving feature recognition and enhancing the fault characteristics of rolling bearings.Finally,the ex⁃cellent denoising ability of the method in the case of strong background noise,which can enhance weak fault fea⁃tures and suppress irrelevant components,are proved by simulation signal and experimental data analysis of roll⁃ing bearing failure.

关 键 词：滚动轴承 特征增强 变分模态分解 1.5维谱 加权谐噪比 

分 类 号：TH133.3[机械工程—机械制造及自动化] TH17