高故障特征频率情况下滚动轴承故障诊断  

Fault Diagnosis of Rolling Bearing with High Fault Characteristic Frequency

作　　者：印爱民 郭浩天 孙聪 YIN Ai-min;GUO Hao-tian;SUN Cong(Aero Engine Control System Institute,AECC,Wuxi 214000,China)

机构地区：[1]中国航发控制系统研究所,江苏无锡214000

出　　处：《航空计算技术》2025年第2期18-22,共5页Aeronautical Computing Technique

基　　金：CXPT-2019-043专项资金项目资助。

摘　　要：滚动轴承是旋转机械中的关键部件。通过对轴承振动信号进行包络分析,可实现轴承故障诊断。针对轴承故障特征频率较高的旋转机械,分析了常用包络分析方法存在对高故障特征频率信号处理能力不足的问题,提出了一种基于梳状滤波的滚动轴承故障诊断方法。所提方法与传统包络分析方法的区别在于解包络前采用梳状滤波对信号进行处理。经仿真验证和实际大型旋转机械故障轴承信号的实验验证表明,相比于现有其他方法,所提方法处理低信噪比高轴承故障特征频率的大型旋转机械轴承信号具有一定的优越性,具备工程实践价值。Rolling bearings are critical components in rotating machinery.Envelope analysis of bearing vibration signals enables fault diagnosis.However,conventional envelope analysis methods struggle with high fault characteristic frequencies in rolling bearings.This paper addresses this issue by proposing a fault diagnosis method based on comb filtering for rolling bearings vibration signals.The key difference of the proposed method lies in the use of comb filtering to preprocess the signal before demodulation.Experimental verification through simulations and actual fault signals from large-scale rotating machinery demonstrates that the proposed method outperforms existing approaches in handling low-SNR,high-fault characteristic frequency signals,thereby holding practical engineering value.

关 键 词：滚动轴承 故障诊断 包络分析 梳状滤波 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]