| 余永增.滚动轴承声信号特征提取和诊断试验研究[J].,2018,37(6):889-894 |
| 滚动轴承声信号特征提取和诊断试验研究 |
| Experimental research on feature extraction and diagnostics for acoustic emission signals of rolling bearings |
| 投稿时间：2018-01-09 修订日期：2018-10-31 |
| 中文摘要: |
| 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。 |
| 英文摘要: |
| 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. |
| DOI：10.11684/j.issn.1000-310X.2018.06.009 |
| 中文关键词: 滚动轴承，声发射，总体平均经验模式分解，能量矩，Hilbert边际谱 |
| 英文关键词: Rolling bearings, Acoustic emission, Ensemble empirical mode decomposition, Energy moments, Hilbert marginal spectrum |
| 基金项目: |
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