增强组合差分乘积形态学滤波的轴承故障特征提取方法
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长安大学 电子与控制工程学院,西安 710064

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中图分类号:

TH113???????

基金项目:

陕西省自然科学基础研究计划资助项目(No. 2019JQ-678);陕西省重点研发计划(No. 2021GY-098);西安市智慧高速公路信息融合与控制重点实验室(No. ZD13CG46);长安大学中央高校基本科研业务费专项资金资助(No. 300102321504,300102321501,300102321503)


Bearing Fault Feature Extraction Method Based on Enhanced Combination Difference Multiply Morphological Filter
Author:
Affiliation:

School of Electronic & Control Engineering, Chang’an University, Xi’an 710064, P.R.China

Fund Project:

The Natural Science Basic Research Program of Shaanxi (Grant No.2019JQ-678); The key research and development program of Shaanxi Province (Grant No. 2021GY-098); The Xi’an Key Laboratory on Intelligent Highway Information Fusion and Controlling (Grant No. ZD13CG46); The Fundamental Research Funds for the Central Universities, CHD (Grant No. 300102321504, 300102321501, 300102321503)

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    摘要:

    针对滚动轴承故障信号具有非线性、非平稳、强噪声特性导致常规时频域特征提取方法受限问题,提出一种增强组合差分乘积形态学滤波的轴承故障特征提取方法。在分析数学形态学四种基本运算的正、负冲击脉冲提取特性的基础上,对其进行级联、差分、乘积构造的一种新的组合差分乘积算子(CDMO)具备了同时提取正、负冲击脉冲的能力,并发挥梯度乘积运算对脉冲提取更为敏感的优势实现故障信息的充分提取。引入故障特征频率比指标优化CDMO结构元素参数,修正待处理信号的几何特征,提取与结构元素相匹配的信号特征信息。在CDMO滤波的基础上,借助三阶累积量切片谱技术能够抑制高斯噪声、突出二次耦合分量的优势,准确提取故障特征频率及其倍频,增强轴承故障特征提取能力并抑制噪声干扰。依托两种不同来源的工程实际信号并与经典故障特征提取方法对比分析,验证了所提方法的有效性。

    Abstract:

    Aiming at the problem that the conventional time-frequency domain feature extraction method is limited due to the non-linear, non-stationary and strong noise characteristics of the rolling bearing fault signal, a bearing fault feature extraction method based on enhanced combination difference multiply morphological filter is proposed. Based on the analysis of the positive and negative shock pulse extraction characteristics of the four basic operations of mathematical morphology, the cascade, difference, and multiply are constructed to construct a new combination difference multiply operator (CDMO) with the ability to simultaneous extraction of positive and negative shock pulses, and take advantage of the gradient multiply operation that is more sensitive to pulse extraction, to achieve sufficient extraction of fault information. Introduce the fault characteristic frequency ratio index to optimize the parameters of the CDMO structural elements, modify the geometric characteristics of the signal to be processed, and extract the signal characteristic information that matches the structural elements. On the basis of CDMO filtering, with the help of third-order cumulant slice spectrum technology which can suppress gaussian noise and highlight the advantages of secondary coupling components, accurately extract fault feature frequencies and their multiplications, enhance the ability of bearing fault feature extraction and suppress noise interference. Relying on actual engineering signals from two different sources and comparing with classic fault feature extraction methods, the effectiveness of the proposed method is verified.

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  • 收稿日期:2021-12-01
  • 最后修改日期:2022-01-06
  • 录用日期:2022-01-07
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