摘要
针对滚动轴承复合故障模式下的微弱特征难以提取的问题,提出了基于自适应迭代滤波(adaptive iterative filtering,简称AIF)和改进的时时变换(time-time transform,简称TT)的滚动轴承复合故障诊断方法。首先,采用AIF将信号分解,得到一系列本征模态分量,并以最大相关峭度作为评价准则,筛选出其中的特征分量,实现滚动轴承复合故障振动信号的特征分离;其次,利用改进的时时变换方法对特征分量进行降噪,增强特征分量的冲击特征;最后,对降噪的特征分量进行包络谱分析,提取故障特征频率,实现滚动轴承故障模式的精确判别。仿真实验和故障诊断实例表明,该方法可以有效提取滚动轴承复合故障模式下的微弱特性信息。
For the problem that the weak features of rolling bearing compound failure mode are difficult to ex⁃tract,a method for rolling bearing compound fault diagnosis based on adaptive iterative filtering(AIF)and im⁃proved time-time(TT)transform is proposed.First,AIF method is applied to decompose the fault vibration signal to achieve a series of intrinsic mode functions and the maximum correlated kurtosis criterion is adopted to select the characteristic components.Then,the improved TT transform method is used to denoise the character⁃istic components to reinforce the impact features.Finally,the denoised characteristic components are performed on envelope analysis to extract the fault characteristic frequencies and complete the identification of rolling bear⁃ing fault mode.The simulated test and actual fault diagnosis instance show that the method proposed in this arti⁃cle can effectively identify the weak fault feature message of the fault signals.
作者
刘宝华
张穆勇
臧延旭
唐贵基
LIU Baohua;ZHANG Muyong;ZANG Yanxu;TANG Guiji(School of Mechanical and Electrical Engineering,North China Institute of Aerospace Engineering Langfang,065000,China;Department of Mechanical Engineering,North China Electric Power University Baoding,071003,China)
出处
《振动．测试与诊断》
EI
CSCD
北大核心
2022年第6期1206-1211,1249,共7页
Journal of Vibration,Measurement & Diagnosis
基金
国家自然科学基金资助项目(51777074)
北华航天工业学院博士科研基金资助项目(BKY-2020-015)
廊坊市科学技术研究与发展计划资助项目(2020011047)。
关键词
滚动轴承
自适应迭代滤波
时时变换
复合故障
故障诊断
rolling bearing
adaptive iterative filtering
time-time transform
compound fault
fault diagnosis
作者简介
第一作者:刘宝华,男,1981年8月生,硕士、副教授。主要研究方向为航天产品结构可靠性测试技术及机电系统创新设计。E⁃mail:382774027@qq.com;通信作者:张穆勇,男,1977年7月生,博士、讲师。主要研究方向为动力机械状态监测及故障诊断,机电系统可靠性分析。E⁃mail:zhangmuyong@126.com。
