ROLLING BEARING FAULT FEATURE EXTRACTION RESEARCH BASED ON IMPROVED CEEMDAN AND RECONSTRUCTION

LIANG Kai; LIU Tao; MA PeiYuan; WU Xing

doi:10.16579/j.issn.1001.9669.2019.03.005

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ROLLING BEARING FAULT FEATURE EXTRACTION RESEARCH BASED ON IMPROVED CEEMDAN AND RECONSTRUCTION

更新时间：2022-09-22

- ROLLING BEARING FAULT FEATURE EXTRACTION RESEARCH BASED ON IMPROVED CEEMDAN AND RECONSTRUCTION
- Journal of Mechanical Strength Vol. 41, Issue 3, Pages: 532-539(2019)
- 作者机构：
  
  1. 昆明理工大学机电工程学院
- 作者简介：
- 基金信息：
- DOI：10.16579/j.issn.1001.9669.2019.03.005
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LIANG Kai, LIU Tao, MA PeiYuan, et al. ROLLING BEARING FAULT FEATURE EXTRACTION RESEARCH BASED ON IMPROVED CEEMDAN AND RECONSTRUCTION. [J]. 41(3):532-539(2019)
DOI：

LIANG Kai, LIU Tao, MA PeiYuan, et al. ROLLING BEARING FAULT FEATURE EXTRACTION RESEARCH BASED ON IMPROVED CEEMDAN AND RECONSTRUCTION. [J]. 41(3):532-539(2019) DOI： 10.16579/j.issn.1001.9669.2019.03.005.

摘要

滚动轴承作为旋转设备的关键部件,其性能严重影响设备的运行安全。由于设备工况复杂,反映轴承的故障特征的冲击成分往往被噪声信号所淹没,导致无法有效的提取故障特征。为了更准确的获取滚动轴承的故障信息特征,本文提出一种基于改进的自适应噪声完整集合模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)和峭度指标的特征提取方法。首先,利用改进的CEEMDAN方法对分解过程中的各段信号添加自适应白噪声,计算唯一的余量来获得各个固有模态函数(intrinsic mode function),与EEMD(ensemble empirical mode decomposition)相比,其分解过程完整。其次,计算各IMF分量的峭度指标,筛选重构IMF分量集,然后利用重构信号峭度最优指标筛选出最合适的重构信号,最后,通过包络解调获得轴承故障特征。结果表明,该方法具有更好的分解效果,自适应性好,可以更好的抑制噪声,提取轴承故障的冲击成分。

Abstract

Rolling bearing as a key component of rotating equipment, its performance seriously affect the safe operation of the equipment. As the equipment condition is complex, the impact component of the fault feature is often submerged by the noise signal, therefore the fault feature cannot be extracted effectively. A method was proposed based on improved complete ensemble empirical mode decomposition with adaptive noise（CEEMDAN） and kurtosis index by this paper. Firstly, the improved CEEMDAN method is used to add adaptive white noise to each signal in the decomposition process, a unique residue was computed to obtain each intrinsic model function（IMF）, compared with ensemble empirical mode decomposition（EEMD）, the decomposition is complete. Secondly, the kurtosis index is calculated of each IMF to select the reconstructed IMF component and the kurtosis index is used to select the most suitable reconstructed signal. Finally, the bearing fault feature is obtained by envelope demodulation. The results confirm that this method has better decomposition effect, better adaptability and highlight the impact of the bearing fault.

关键词

CEEMDAN峭度信号重构滚动轴承故障诊断

Keywords

CEEMDANKurtosisSignal reconstructionRolling bearingFault diagnosis

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