Application of Maximum Overlap Discrete Wavelet Packet Transform Marginal Spectrum Features in Gear Fault Diagnosis

doi:10.7652/xjtuxb202002005

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Application of Maximum Overlap Discrete Wavelet Packet Transform Marginal Spectrum Features in Gear Fault Diagnosis

更新时间：2025-05-08

- Application of Maximum Overlap Discrete Wavelet Packet Transform Marginal Spectrum Features in Gear Fault Diagnosis
- Vol. 54, Issue 2, Pages: 35-42(2020)
- 作者机构：
  
  1. 三峡大学水电机械设备设计与维护湖北省重点实验室,湖北,宜昌,443002
  2. 三峡大学机械与动力学院,湖北,宜昌,443002
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202002005
  CLC： TH113;TH165
- Online First：10 February 2020，
  
  Published：2020
- 稿件说明：
移动端阅览
Application of Maximum Overlap Discrete Wavelet Packet Transform Marginal Spectrum Features in Gear Fault Diagnosis[J]. 2020, 54(2): 35-42.
DOI：

Application of Maximum Overlap Discrete Wavelet Packet Transform Marginal Spectrum Features in Gear Fault Diagnosis[J]. 2020, 54(2): 35-42. DOI： 10.7652/xjtuxb202002005.

摘要

针对齿轮故障振动信号多分量频带重叠引发的故障模式混淆问题

提出一种基于最大重叠离散小波包变换(MODWPT)边际谱特征和粒子群优化-支持向量机(PSO-SVM)的故障诊断方法。为了减少谐波及噪声对故障模式分量分离的干扰

首先利用MODWPT将采集到的实验信号进行5层分解

得到32个分量

通过频带能量占优方法

筛选出前16个分量

用来构造信号的希尔伯特边际谱; 然后

将提取的边际谱特征代入PSO参数优化后的SVM

对故障类型进行识别。仿真信号分析结果表明

MODWPT边际谱在抗模式混叠、抗边界效应和频率提取准确性方面都要优于EMD方法。通过对6种不同类型的齿轮故障信号进行分析

MODWPT边际谱归一化特征具有明显的故障类型分层现象

对齿轮故障的识别准确率达到98%

说明该方法具有较强的故障诊断能力。

Abstract

A fault diagnosis method based on the maximum overlap discrete wavelet packet transform(MODWPT)marginal spectrum features and the particle swarm optimi-ation-support vector machine(PSO-SVM)is proposed to deal with the problem of fault mode confusion caused by multi-component band overlap of gear fault vibration signals. In order to reduce the influence of harmonics and noise on the fault mode component separation

MODWPT is used to decompose the collected experimental signals into 5 layers and to obtain 32 components. The first 16 components are selected based on the principle of the band energy dominant distribution to construct a Hilbert marginal spectrum. Then

the marginal spectrum features are extracted and considered as an input to the SVM with PSO parameter optimi-ation for fault type identification. Analysis results of simulation signals show that the MODWPT marginal spectrum is superior to the EMD method in terms of anti-mode mixing

anti-end effect

and frequency extraction a

关键词

Keywords

references

HUANG N E, SHEN Z, LONG S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis [EB/OL]. [2019-03-15]. https:∥doi.org/10.1098/rspa.1998.0193.

程军圣. 基于Hilbert-Huang变换的旋转机械故障诊断方法研究 [D]. 长沙: 湖南大学, 2005: 78-80.

张琛, 赵荣珍, 邓林峰. 基于EEMD奇异值熵的滚动轴承故障诊断方法 [J]. 振动、测试与诊断, 2019, 39(2): 353-358.

ZHANG Chen, ZHAO Rongzhen, DENG Linfeng. Fault diagnosis method of rolling bearing based on EEMD singular value entropy [J]. Journal of Vibration, Measurement Diagnosis, 2019, 39(2): 353-358.

王志坚, 吴文轩, 马维金, 等. 基于LMD-MS的滚动轴承微弱故障提取方法 [J]. 振动、测试与诊断, 2018, 38(5): 1014-1020.

WANG Zhijian, WU Wenxuan, MA Weijin, et al. LMD-MS-based method for extracting weak faults of rolling bearings [J]. Journal of Vibration, Measurement Diagnosis, 2018, 38(5): 1014-1020.

CHEN B, HE Z, CHEN X, et al. A demodulating approach based on local mean decomposition and its applications in mechanical fault diagnosis [J]. Measurement Science Technology, 2011, 22(5): 055704.

DRAGOMIRETSKIY K, ZOSSO D. Variational mode decomposition [J]. IEEE Transactions on Signal Processing, 2014, 62(3): 531-544.

CICONE A, LIU J, ZHOU H. Adaptive local iterative filtering for signal decomposition and instantaneous frequency analysis [J]. Applied and Computational Harmonic Analysis, 2016, 41(2): 384-411.

李奕江, 张金萍, 李允公. 基于VMD-HMM的滚动轴承磨损状态识别 [J]. 振动与冲击, 2018, 37(21): 61-67.

LI Yijiang, ZHANG Jinping, LI Yungong. Wear state recognition of rolling bearings based on VMD-HMM [J]. Journal of Vibration and Shock, 2018, 37(21): 61-67.

陈保家, 汪新波, 赵春华, 等. 基于自适应局部迭代滤波和能量算子解调的滚动轴承故障特征提取 [J]. 南京理工大学学报, 2018, 42(4): 445-452.

CHEN Baojia, WANG Xinbo, ZHAO Chunhua, et al. Fault feature extraction of rolling bearing based on ALIF and energy operator demodulation [J]. Journal of Nanjing University of Science and Technology, 2018, 42(4): 445-452.

JIANG X, WANG J, SHI J, et al. A coarse-to-fine decomposing strategy of VMD for extraction of weak repetitive transients in fault diagnosis of rotating machines [J]. Mechanical Systems and Signal Processing, 2019, 116: 668-692.

吕中亮. 基于变分模态分解与优化多核支持向量机的旋转机械早期故障诊断方法研究 [D]. 重庆: 重庆大学, 2016: 49-53.

周航. 基于改进最大重叠离散小波包变换的线性系统参数识别 [D]. 南京: 南京航空航天大学, 2018: 11-28.

杨宇, 何怡刚, 程军圣, 等. 用最大重叠离散小波包变换的Hilbert谱时频分析 [J]. 振动、测试与诊断, 2009, 29(1): 10-13.

YANG Yu, HE Yigang, CHENG Junsheng, et al. Time-frequency analysis of Hilbert spectrum based on maximal overlap discrete wavelet packet transform [J]. Journal of Vibration, Measurement Diagnosis, 2009, 29(1): 10-13.

CHEN Baojia, SHEN Baoming, CHEN Fafa, et al. Fault diagnosis method based on integration of RSSD and wavelet transform to rolling bearing [J]. Measurement, 2019, 131(1): 400-411.

CHEN Fafa, YANG Yunpeng, TANG Baoping, et al. Performance degradation prediction of mechanical equipment based on optimized multi-kernel relevant vector machine and fuzzy information granulation [J]. Measurement, 2020, 151(2): 107116.

CRISTIANINI N, SHAWETAYLOR J. An introduction to support vector machines and other kernel-based learning methods [J]. Kybernetes, 2001, 30(1): 103-115.

YUAN S, CHU F. Fault diagnostics based on particle swarm optimization and support vector machines [J]. Mechanical Systems Signal Processing, 2007, 21(4): 1787-1798.

YIN Z, Hou J. Recent advances on SVM based fault diagnosis and process monitoring in complicated industrial processes [J]. Neurocomputing, 2016, 174(1): 643-650.

梁凯. 基于深度学习的风电机组齿轮箱故障特征提取和智能分类研究 [D]. 河北秦皇岛: 燕山大学, 2017: 16-22.

陈保家, 邱光银, 肖文荣, 等. 航空发动机转子轴承运行可靠性评估方法 [J]. 西安交通大学学报, 2018, 52(10): 41-48.

CHEN Baojia, WANG Xinbo, XIAO Wenrong, et al. Operational reliability evaluation method to aero-engine rotor bearings [J]. Journal of Xi’an Jiaotong University, 2018, 52(10): 41-48.

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