自适应MCKD结合Autogram的矿用滚动轴承故障特征提取

申勇; 章翔峰; 姜宏; 周建; 汪皖; 蒋艺峰; 毕君东

doi:10.13568/j.cnki.651094.651316.2023.12.06.0001

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自适应MCKD结合Autogram的矿用滚动轴承故障特征提取

工程技术 | 更新时间：2026-01-21

- 自适应MCKD结合Autogram的矿用滚动轴承故障特征提取
- 自适应MCKD结合Autogram的矿用滚动轴承故障特征提取
- 新疆大学学报（自然科学版中英文） 2024年41卷第4期页码：505-512
- 作者机构：
  
  1. 长沙矿山研究院有限责任公司金属矿山安全技术国家重点实验室
  2. 新疆大学智能制造现代产业学院
- 作者简介：
- 基金信息：
  
  国家自然科学基金“大型风电机组主传动链同频干扰状态下复合故障信息流漂变及传递规律研究”（51865054）,“含单源劣化之变的风力机齿轮箱复合故障浸润机制研究”（52265016）;湖南省重点领域研发计划“矿山斜井轨道运输安全应急保障技术及装备研究”（2022SK2092）
- DOI：10.13568/j.cnki.651094.651316.2023.12.06.0001
  中图分类号： TD407
- 纸质出版：2024
- 稿件说明：
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[1]申勇,章翔峰,姜宏,等.自适应MCKD结合Autogram的矿用滚动轴承故障特征提取[J].新疆大学学报(自然科学版中英文),2024,41(04):505-512.
[1]申勇,章翔峰,姜宏,等.自适应MCKD结合Autogram的矿用滚动轴承故障特征提取[J].新疆大学学报(自然科学版中英文),2024,41(04):505-512. DOI： 10.13568/j.cnki.651094.651316.2023.12.06.0001.

DOI：10.13568/j.cnki.651094.651316.2023.12.06.0001.

摘要

为辨析矿用滚动轴承运行状态，有效地提取矿用滚动轴承故障特征，提出了一种基于粒子群优化算法（Particle Swarm Optimization

PSO）的参数自适应优化最大相关峭度解卷积算法（Maximum Correlated Kurtosis Deconvolution

MCKD）与自相关谱峭度图法（Autogram）相结合的矿用滚动轴承故障特征提取算法．首先，在考虑振动信号的强周期性的基础上，采用MCKD对原始信号进行预处理以实现信号的降噪与增强；同时，针对MCKD参数选择问题，构造利用PSO对适应度函数进行寻优得到合适的参数组合[滤波长度L，解卷积周期T]；此后，利用Autogram对处理后信号进行特征提取；最后，通过仿真信号及公开数据集试验信号对该算法进行验证．结果表明：PSO-MCKD-Autogram算法能够较好地克服噪声影响，可有效提取矿用滚动轴承故障特征且具有一定的鲁棒性．

Abstract

In order to analyze the operation state of mining rolling bearings and effectively extract the fault characteristics of mining rolling bearings

a parameter adaptive optimization Maximum Correlated Kurtosis Deconvolution(MCKD) combined with Autogram is proposed based on Particle Swarm Optimization(PSO). MCKD combined with Autogram as a fault feature extraction algorithm for mining bearings. Firstly

based on the strong periodicity of the vibration signal

MCKD is used to preprocess the original signal to realize the noise reduction and enhancement of the signal; At the same time

in view of the MCKD parameter selection problem

PSO is constructed to optimize the fitness function to obtain the suitable parameter combination [filter length L

deconvolution period T ]; Thereafter

Autogram is used to extract the features of the processed signal. Finally

the algorithm is validated by simulation signals and experimental signals from public datasets. The results show that the PSOMCKD-Autogram algorithm can better overcome the influence of noise

and can effectively extract the fault features of mining bearings with certain robustness. The results can provide theoretical basis for condition monitoring and fault analysis of rolling bearings in mining.

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Keywords

references

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