一种滚动轴承早期故障特征提取方法  

A Method for Extracting Early Fault Features of Rolling Bearings

作　　者：崔奔 张文斌[2] 郭盼盼 CUI Ben;ZHANG Wenbin;GUO Panpan(School of Mechanical and Electrical Engineering,Kunming University of Science and Technology,Kunming 650500,China;Key Laboratory of Mechanical Performance Analysis and Optimization of Plateau in Yunnan Province,College of Engineering,Honghe University,Mengzi 661199,Yunnan China)

机构地区：[1]昆明理工大学机电工程学院,昆明650500 [2]红河学院工学院云南省高校高原机械性能分析与优化省重点实验室,云南蒙自661199

出　　处：《噪声与振动控制》2023年第6期95-100,156,共7页Noise and Vibration Control

基　　金：国家自然科学基金资助项目(51769007);云南省地方本科高校基础研究联合专项重点资助项目(202001BA070001-002);兴滇英才支持计划资助项目经费支持(YNWR-QNBJ-2018-349)。

摘　　要：针对滚动轴承早期故障受噪声污染大,故障特征频率难以提取而导致的故障诊断困难问题,提出一种新的滚动轴承早期故障特征提取方法。首先利用泰尔指数(Theil Index,TI)对滚动轴承进行健康状态评估,并检测出状态异常的信号;然后对异常信号进行奇异谱分解(Singular Spectrum Decomposition,SSD)得到若干分量,以方差贡献度为标准筛选出最佳分量;最后通过蜜獾算法(Honey Badger Algorithm,HBA)优化的最大相关峭度解卷积(Maximum Correlated Kurtosis Deconvolution,MCKD)对最佳分量进行降噪处理,并对降噪处理后的信号进行包络解调分析提取出故障特征频率。通过两个数据集的分析,验证所提方法的有效性。Aiming at the problem that the early fault of rolling bearings is interfered by noise pollution and it is difficult to extract the fault characteristic frequency,a new method for extracting early fault features of rolling bearings is proposed.Firstly,Theil index(TI)is used to evaluate the health status of the rolling bearings and detect abnormal state signals.Secondly,singular spectrum decomposition(SSD)is carried out for the abnormal signals to obtain several components,and the best component is selected according to the variance contribution degree.Lastly,the optimal component is denoised by means of the maximum correlation kurtosis deconvolution(MCKD)and optimized by the honey badger algorithm(HBA).And the envelope demodulation analysis of the denoised signal is performed to extract the fault characteristic frequency.Through the analysis of two datasets,the effectiveness of the proposed method is verified.

关 键 词：故障诊断 泰尔指数 奇异谱分解 蜜獾算法 最大相关峭度解卷积 滚动轴承 

分 类 号：TH133.3[机械工程—机械制造及自动化] TH113