[1]雷文平,宋圣霖,郝旺身,等.基于FV-FBE的滚动轴承故障诊断研究[J].郑州大学学报(工学版),2020,41(05):82-86.[doi:10.13705/j.issn.1671-6833.2020.03.020]
　LEI Wenping,SONG Shenglin,HAO Wangshen,et al.Fault Diagnosis of Rolling Bearing Based on FV-FBE[J].Journal of Zhengzhou University (Engineering Science),2020,41(05):82-86.[doi:10.13705/j.issn.1671-6833.2020.03.020]
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基于FV-FBE的滚动轴承故障诊断研究()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
41
期数:
2020年05期
页码:
82-86
栏目:
出版日期:
2020-10-01

文章信息/Info

Title:
Fault Diagnosis of Rolling Bearing Based on FV-FBE
作者:
雷文平; 宋圣霖; 郝旺身; 陈宏; 胡鑫
郑州大学机械与动力工程学院,河南郑州450001, 郑州恩普特科技股份有限公司,河南郑州450001

Author(s):
LEI Wenping1;  SONG Shenglin1;  HAO Wangshen1;  CHEN Hong1;  HU Xin2
1.College of Mechanical and Power Engineering, Zhengzhou University,Zhengzhou 450001， China; 2.Zhengzhou Expert Technology Co.,Ltd.,Zhengzhou 450001， China
关键词:
Keywords:
full vector spectrum;  frequency band entropy;  fault diagnosis;  rolling bearing
DOI:
10.13705/j.issn.1671-6833.2020.03.020
文献标志码:
A
摘要:
针对单通道信号不能全面提取旋转机械的振动信息,为了从强背景噪声中准确提取出滚动轴承的微弱故障特征,提出了一种全矢频带熵(FV-FBE)的滚动轴承故障诊断算法。该方法采用短时傅里叶变换计算频带熵(FBE) ,根据FBE最小原则自适应设计双通道信号的带通滤波器带宽和中心频率,对滤波后的双通道信号采用全矢Hilbert 包络解调,得到全矢包络谱进行滚动轴承的故障识别。实验结果表明:FV-FBE算法可以全面准确地提取滚动轴承故障特征,优于谱峭度算法得到的全矢包络谱,抗干扰能力强。
Abstract:
It was not comprehensive to extract the vibration signal of rotating machinery from single channel signal. In order to extract the weak fault features of rolling bearing accurately from the strong background noise, a fault diagnosis algorithm of rolling bearing based on full vector frequency band entropy(FV-FBE) was proposed in this paper. The short time Fourier transform was used to calculate the frequency band entropy(FBE). And according to the FBE minimum principle, the bandwidth and center frequency of the dual channel signal band-pass filter were designed adaptively. The filtered dual channel signal was demodulated by the full vector Hilbert envelope, and the full vector envelope spectrum was obtained for fault diagnosis of rolling bearing. The experimental results showed that the FV-FBE algorithm could extract the fault features of rolling bearing comprehensively and accurately, which was better than the full vector envelope spectrum obtained by spectral kurtosis algorithm and had strong anti-interference ability.

参考文献/References:

[1] 郝伟,林辉翼,郝旺身,等. 基于全矢稀疏编码的滚动轴承故障识别方法[J]. 郑州大学学报(工学版),2019,40(3):31-35.

[2] 李凌均,金兵,马艳丽,等. 基于MEMD与MMSE的滚动轴承退化特征提取方法[J]. 郑州大学学报(工学版)，2018,39(4):86-91.
[3] TYAGI S, PANIGRAHI S K. An improved envelope detection method using particle swarm optimisation for rolling element bearing fault diagnosis[J]. Journal of computational design and engineering,2017,4(4):305-317.
[4] KLAUSEN A, ROBBERSMYR K G, KARIMI H R. Autonomous bearing fault diagnosis method based on envelope spectrum[J]. IFAC-PapersOnLine,2017,50(1)：13378-13383.[5] CHEGINI S N, BAGHERI A, NAJAFI F. Application of a new EWT-based denoising technique in bearing fault diagnosis[J]. Measurement, 2019,144:275-297.
[6] KUMAR K, SHUKLA S, SINGH S K. A combined approach for weak fault signature extraction of rolling element bearing using Hilbert envelop and zero frequency resonator[J]. Journal of sound and vibration, 2018,419:436-451.
[7] 陈东毅,陈建国,李玉榕. 改进经验模态分解与谱峭度法的步态信号特征分析[J]. 福州大学学报(自然科学版),2019,47(5):630-634.
[8] 田晶,王英杰,刘丽丽,等. 基于Birge-Massart阈值降噪与EEMD及谱峭度的滚动轴承故障特征提取[J].航空动力学报,2019,34(6):1399-1408.
[9] LEI Y G,LIN J,HE Z J,et al. Application of an improved kurtogram method for fault diagnosis of rolling element bearings[J]. Mechanical systems and signal processing,2011,25(5):1738-1749.
[10] LI H, LIU T, WU X,et al. Application of EEMD and improved frequency band entropy in bearing fault feature extraction[J]. ISA transactions,2019,88：170-185.
[11] 李华,刘韬,伍星，等. 基于SVD和熵优化频带熵的滚动轴承故障诊断研究[J].振动工程学报,2018,31(2):358-364.

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更新日期/Last Update: 2020-10-23