Feature Extraction of Gearbox Composite Fault Based on the Improved Multipoint Optimal Minimum Entropy Deconvolution

doi:10.7652/xjtuxb202005010

您当前的位置：

首页 >

文章列表页 >

Feature Extraction of Gearbox Composite Fault Based on the Improved Multipoint Optimal Minimum Entropy Deconvolution

更新时间：2025-07-09

- Feature Extraction of Gearbox Composite Fault Based on the Improved Multipoint Optimal Minimum Entropy Deconvolution
- Vol. 54, Issue 5, Pages: 70-77+94(2020)
- 作者机构：
  
  沈阳理工大学汽车与交通学院,沈阳,110159
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202005010
  CLC： TH113.1
- Online First：10 May 2020，
  
  Published：2020
- 稿件说明：
移动端阅览
Feature Extraction of Gearbox Composite Fault Based on the Improved Multipoint Optimal Minimum Entropy Deconvolution[J]. 2020, 54(5): 70-77+94.
DOI：

Feature Extraction of Gearbox Composite Fault Based on the Improved Multipoint Optimal Minimum Entropy Deconvolution[J]. 2020, 54(5): 70-77+94. DOI： 10.7652/xjtuxb202005010.

摘要

针对强背景噪声下齿轮箱复合故障中的微弱故障特征难以提取的问题

提出了一种改进多点最优最小熵反褶积的齿轮箱复合故障特征提取方法。将小波降噪作为前置滤波器

克服了多点峭度谱在强背景噪声下难以有效识别信号中的微弱故障周期成分的缺点

使信号峭度增加了65.9%

突出了微弱故障周期成分; 根据多点峭度谱识别出的故障周期成分设置合理的故障区间

利用多点最优最小熵反褶积突出了信号中的故障周期

避免了对信号直接包络解调而出现的漏诊现象; 将差分能量算子解调应用于改进算法处理后的信号

与传统的Hilbert解调方法相比

该算法得到的解调谱中故障特征频率的峰值更加明显。通过对仿真信号与齿轮箱中齿轮点蚀磨损复合故障振动信号的研究结果表明

改进多点最优最小熵反褶积方法能够完整地提取信号中的故障特征频率成分

成功率达到了100%; 与变分模态分解进行了对比分析

改进算法有效消除了模态混叠现象。仿真和试验结果表明

改进算法可以有效提取强背景噪声下齿轮箱复合故障中的微弱故障特征。

Abstract

In order to solve the problem that it is difficult to extract the weak feature from gearbox complex fault signal under strong background noise

an improved multipoint optimal minimum entropy deconvolution method is proposed. The wavelet denoising is used as a pre-filter to effectively identify the weak fault period components in the multipoint kurtosis spectrum under strong background noise. The kurtosis of the signal is increased by 65.9% and the weak fault period components are highlighted. According to the fault period components identified by the multipoint kurtosis spectrum

a reasonable fault interval is set

and the fault period in the signal is enhanced by using the multipoint optimal minimum entropy deconvolution

so that the missed diagnosis caused by the direct envelope demodulation of the signal is avoided. The differential energy operator demodulation is applied to the signal enhanced by the improved algorithm. Compared with the Hilbert demodulation method

the improved al

关键词

Keywords

references

王志坚, 王俊元, 赵志芳, 等. 基于MKurt-MOMEDA的齿轮箱复合故障特征提取 [J]. 振动、测试与诊断, 2017, 37(4): 830-834.

WANG Zhijian, WANG Junyuan, ZHAO Zhifang, et al. Feature extraction of gearbox composite fault based on MKurt-MOMEDA [J]. Journal of Vibration, Measurement and Diagnosis, 2017, 37(4): 830-834.

LIU Jing, SHAO Yimin, LIM T C. Vibration analysis of ball bearing with a localized defect applying piecewise response function [J]. Mechanism and Machine Theory, 2012, 56(1): 156-169.

WANG Yanxue, LIANG Ming. Identification of multiple transient faults based on the adaptive spectral kurtosis method [J]. Journal of Sound and Vibration, 2012, 331(2): 470-486.

JIANG Hongkai, LI Chengliang, LI Huaxing. An improved EEMD with multiwavelet packet for rotating machinery multi-fault diagnosis [J]. Mechanical Systems and Signal Processing, 2013, 36(2): 225-239.

WIGGINS R A. Minimum entropy deconvolution [J]. Geoexploration, 1978, 16(1/2): 21-35.

周士帅, 窦东阳, 薛斌. 基于LMD和MED的滚动轴承故障特征提取方法 [J]. 农业工程学报, 2016, 32(23): 70-76.

ZHOU Shishuai, DOU Dongyang, XUE Bin. Fault feature extraction method of rolling bearing based on LMD and MED [J]. Transactions of the Chinese Society of Agricultural Engineering, 2016, 32(23): 70-76.

王朝阁, 刘桐桐, 任学平, 等. 双树复小波和MED在齿轮箱早期故障诊断中的应用 [J]. 机械强度, 2018, 40(2): 280-286.

WANG Chaoge, LIU Tongtong, REN Xueping, et al. Application of double tree complex wavelet and MED in early fault diagnosis of gearbox [J]. Journal of Mechanical Strength, 2018, 40(2): 280-286.

DU Wenhua, GUO Xiaoming, HAN Xiaofeng, et al. Application of a novel adaptive med fault diagnosis method in gearboxes [J]. Entropy, 2019, 21(11): 1106.

MCDONALD G L, ZHAO Qing, ZUO Mingjian. Maximum correlated kurtosis deconvolution and application on gear tooth chip fault detection [J]. Mechanical Systems and Signal Processing, 2012, 33(1): 237-255.

何群, 郭源耕, 王霄, 等. 基于信号共振稀疏分解和最大相关峭度解卷积的齿轮箱故障诊断 [J]. 中国机械工程, 2017, 28(13): 1528-1534.

HE Qun, GUO Yuangeng, WANG Xiao, et al. Gearbox fault diagnosis based on signal resonance sparse decomposition and maximum correlation kurtosis deconvolution [J]. China Mechanical Engineering, 2017, 28(13): 1528-1534.

李煌, 孟恩隆, 王灵梅, 等. 基于LMD与MCKD的滚动轴承早期故障诊断方法 [J]. 噪声与振动控制, 2018, 38(4): 186-191.

LI Huang, MENG Enlong, WANG Lingmei, et al. Early fault diagnosis method of rolling bearing based on LMD and MCKD [J]. Noise and Vibration Control, 2018, 38(4): 186-191.

LI Zhuorui, MA Jun, WANG Xiaodong, et al. MVMD-MOMEDA-TEO model and its application in feature extraction for rolling bearings [J]. Entropy, 2019, 21(4): 331.

MCDONALD G L, ZHAO Qing. Multipoint optimal minimum entropy deconvolution and convolution fix: application to vibration fault detection [J]. Mechanical Systems and Signal Processing, 2016, 82: 461-477.

ZHAO Lei, ZHANG Yongxiang, ZHU Danchen. Feature extraction for rolling element bearing weak fault based on MOMEDA and ICEEMDAN [J]. Journal of Vibroengineering, 2018, 20(6): 2352-2362.

LI Chao, GUO Yu, XIAO Zhengming, et al. Fault feature extraction of planet gear tooth crack based on impulsive feature enhancement by MOMEDA [J]. Journal of Intelligent and Fuzzy Systems, 2019, 36(2): 943-953.

王龙, 沈艳霞, 季凌燕. 基于小波降噪和EMD方法的风力发电系统齿轮箱故障诊断 [J]. 江南大学学报(自然科学版), 2012, 11(2): 159-162.

WANG Long, SHEN Yanxia, JI Lingyan. Gearbox fault diagnosis of wind power generation system based on wavelet denoising and EMD [J]. Journal of Southern Yangtze University(Nature Science Edition), 2012, 11(2): 159-162.

张雄希, 刘振兴. 共振解调与小波降噪在电机故障诊断中的应用 [J]. 电机与控制学报, 2010, 14(6): 66-70.

ZHANG Xiongxi, LIU Zhenxing. Application of resonance demodulation and wavelet de-noising in motor fault diagnosis [J]. Electric Machines and Control, 2010, 14(6): 66-70.

卓蒙蒙, 张晓光, 姬程鹏, 等. 基于小波降噪与分形理论的滚动轴承故障诊断 [J]. 轴承, 2011, 6: 35-37.

ZHUO Mengmeng, ZHANG Xiaoguang, JI Chengpeng, et al. Fault diagnosis of rolling bearing based on wavelet de-noising and fractal theory [J]. Bearing, 2011, 6: 35-37.

夏均忠, 于明奇, 白云川, 等. 基于改进信息图与MOMEDA的滚动轴承故障特征提取 [J]. 振动与冲击, 2019, 38(4): 26-32.

XIA Junzhong, YU Mingqi, BAI Yunchuan, et al. Fault feature extraction of rolling bearing based on improved information graph and MOMEDA [J]. Journal of Vibration and Shock, 2019, 38(4): 26-32.

WANG Zhijian, WANG Junyuan, ZHAO Zhifang, et al. A novel method for multi-fault feature extraction of a gearbox under strong background noise [J]. Entropy, 2018, 20(1): 10-26.

唐贵基, 王晓龙. 参数优化变分模态分解方法在滚动轴承早期故障诊断中的应用 [J]. 西安交通大学学报, 2015, 49(5): 73-81.

TANG Guiji, WANG Xiaolong. Parameter optimized variational mode decomposition method with application to incipient fault diagnosis of rolling bearing [J]. Journal of Xi’an Jiaotong University, 2015, 49(5): 73-81.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Energy Efficiency Optimization of the Cryogenic Air Separation Process for Electronic-Grade Ultra Pure Nitrogen

Knowledge Graph Representation Learning Driven by Semantic Discrimination and Subgraph Integration

Integrated and Joint Design Optimization of Centrifugal Compressor Impeller and Casing Treatment at Multiple Working Points

A Fast Calculation Method for Thermal Flow Fields in Transformer Windings Based on PI-DeepONet

Review of the Application of EBSILON Software in Combined Heat and Power System Optimization

Related Author

No data

Related Institution

No data

⁰