参数自适应CYCBD的滚动轴承复合故障特征提取  

Compound fault feature extraction of rolling bearing based on parameters adaptive CYCBD

作　　者：项伟 刘淑杰[1] 李宏坤[1] 曹顺心 吕帅 杨晨 XIANG Wei;LIU Shujie;LI Hongkun;CAO Shunxin;LYU Shuai;YANG Chen(School of Mechanical Engineering,Dalian University of Technology,Dalian Liaoning 116023,China)

机构地区：[1]大连理工大学机械工程学院,辽宁大连116023

出　　处：《航空动力学报》2024年第9期320-328,共9页Journal of Aerospace Power

基　　金：国家重点研发计划项目(2019YFB2004600)。

摘　　要：针对滚动轴承早期故障信号特征难以准确提取与分离问题,提出参数自适应最大2阶循环平稳盲解卷积(CYCBD)的滚动轴承复合故障特征提取方法。基于不同的故障类型,以谐波能量比指标为适应度函数,采用麻雀搜索算法自适应获取解卷积的最佳滤波器长度和循环频率,利用得到的最佳参数组合对原信号中的故障成分逐一提取,并对解卷积后的信号开展包络谱分析,实现轴承复合故障的诊断。分析结果表明:所提出方法能够在强噪声背景下,清晰准确地分离出轴承故障实测信号中的内圈故障频率的1~4倍频及外圈故障的1~6次谐波分量,而其他常用方法只能提取到少数故障频率且分辨能力较低,所提出方法的诊断效果明显,具有更高的应用价值和推广性能。In view of the difficulty to accurately extract and separate the features of the early fault signals of rolling bearings,a compound fault feature extraction method of rolling bearing based on parameters adaptive maximum second-order cyclostationarity blind deconvolution(CYCBD)was proposed.Based on different fault types,the harmonics energy ratio index was used as the fitness function,and the sparrow search algorithm was used to adaptively obtain the optimal filter length and cycle frequency of deconvolution.The obtained optimal parameters combination was used to extract the fault components in the original signal one by one,and the envelope spectrum analysis of the deconvolution signal was carried out to realize the diagnosis of compound fault of the bearing.The analysis results showed that the proposed method can clearly and accurately separate 1—4 times of the inner ring characteristic frequency and 1—6 times harmonic component of the outer ring fault from the measured signal of bearing fault under the background of strong noise,while other common methods can only extract a few fault frequencies with low resolution.The proposed method has obvious diagnostic effect,higher application value and promotion performance.

关 键 词：麻雀搜索算法 最大2阶循环平稳盲解卷积 滚动轴承 复合故障 特征提取 

分 类 号：V229.2[航空宇航科学与技术—飞行器设计] TH133.3[机械工程—机械制造及自动化] TH165.3