Page 59 - 201806

第 37 卷 第 6 期 Vol. 37, No.6
2018 年 11 月 Journal of Applied Acoustics November, 2018

⋄ 研究报告 ⋄

滚动轴承声信号特征提取和诊断试验研究

余永增 †

(中国石油兰州石化公司设备维修公司 兰州 730060)

摘要 为解决振动检测方法不能有效识别低速旋转机械滚动轴承故障问题，利用声发射检测方法，建立了滚
动轴承低速声发射信号采集试验装置，对模拟人工缺陷滚动轴承声发射信号进行了采集，进而对滚动轴承声
发射信号进行总体平均经验模式分解，结合能量矩及相关系数法综合判断分解后各模态分量的真伪，据此提
取出特征信号并做出其局部 Hilbert 边际谱，最后对滚动轴承各种故障模式进行诊断。试验结果表明该诊断方
法能准确识别滚动轴承声发射信号故障频率，依据特征频率及幅值大小可对低速滚动轴承故障进行有效诊断。
关键词 滚动轴承，声发射，总体平均经验模式分解，能量矩，Hilbert 边际谱
中图法分类号: TG115.28 文献标识码: A 文章编号: 1000-310X(2018)06-0889-06
DOI: 10.11684/j.issn.1000-310X.2018.06.009

Experimental research on feature extraction and diagnostics for acoustic

emission signals of rolling bearings

YU Yongzeng

(Petro China Lanzhou Petrochemical Company, Equipment Maintenance Company, Lanzhou 730060, China)

Abstract The acoustic emission method was used to diagnose low-speed rotating mechanical rolling bearing
which vibration detection method can not diagnose effectively. An acoustic emission signal acquisition test
device with low speed for rolling bearing was established in advance. The acoustic emission signals of the
simulated artificial failure bearings were acquired. These signals of rolling bearings were decomposed by
ensemble empirical mode decomposition method. Then the reliability of every intrinsic mode function was
determined by energy moments and correlation coefficients. Characteristic signals were extracted and made to
local Hilbert marginal spectrum. Thus a variety of failure modes of bearing were diagnosed. The experimental
results show that this method can accurately identify the fault frequency of the acoustic emission signal of the
rolling bearing, and according to the characteristic frequency and amplitude can effectively diagnose the fault
of the low-speed rolling bearing.
Key words Rolling bearings, Acoustic emission, Ensemble empirical mode decomposition, Energy moments,
Hilbert marginal spectrum

2018-01-09 收稿; 2018-04-12 定稿
作者简介: 余永增 (1980- ), 男, 甘肃天水人, 硕士, 研究方向: 旋转设备故障诊断与维修。
† 通讯作者 E-mail: yuyongzeng@sina.com