均值优化经验模态分解及其在转子故障诊断中的应用

doi:10.3901/JME.2018.23.093

机械工程学报 ›› 2018, Vol. 54 ›› Issue (23): 93-101.doi: 10.3901/JME.2018.23.093

扫码分享

均值优化经验模态分解及其在转子故障诊断中的应用

郑近德¹, 潘海洋^1,2, 程军圣²

1. 安徽工业大学机械工程学院马鞍山 243032;
2. 湖南大学汽车车身先进设计制造国家重点实验室长沙 410082

收稿日期:2017-11-20 修回日期:2018-05-07 出版日期:2018-12-05 发布日期:2018-12-05
通讯作者: 郑近德(通信作者),男,1986年出生,博士,副教授,硕士研究生导师。主要研究方向为动态信号处理与机械故障诊断。E-mail:jdzheng@ahut.edu.cn
作者简介:潘海洋,男,1989年出生,博士研究生。主要研究方向为模式识别与机械故障诊断。E-mail:pansea@sina.cn;程军圣,男,1968年出生,博士,教授,博士研究生导师。主要研究方向为动态信号处理与机械故障诊断。E-mail:chengjunsheng@hnu.edu.cn
基金资助:
国家自然科学基金（51505002）、国家重点研发计划（2017YFC0805100）、安徽省高校自然科学研究重点（KJ2015A080）资助项目。

Mean-optimized Empirical Mode Decomposition and Its Application in Rotor Fault Diagnosis

ZHENG Jinde¹, PAN Haiyang^1,2, CHENG Junsheng²

1. School of Mechanical Engineering, Anhui University of Technology, Maanshan 243032;
2. State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410082

Received:2017-11-20 Revised:2018-05-07 Online:2018-12-05 Published:2018-12-05

摘要/Abstract

摘要： 经验模态分解（Empirical mode decomposition，EMD）作为一种自适应的信号分解方法已经被广泛应用于诸多工程领域。为了提高EMD的分解性能，分别考虑从不同权值均值曲线的迭代筛分结果中选择正交性最小以及从每层内禀模态函数迭代结果中选择最优以保证整体分解最优，发展了两种均值优化经验模态分解（Mean-optimized empirical mode decomposition，MOEMD）算法。通过仿真信号分析，将MOEMD方法与EMD等现有信号分解方法进行了对比，结果表明，MOEMD方法在分解性能和分解精度方面比EMD等方法有显著提高。最后，将MOEMD方法应用于转子碰摩故障信号分析，并与EMD进行了对比分析，结果表明，MOEMD方法不仅能够有效地识别转子碰摩故障，而且识别效果优于EMD方法。

关键词: 故障诊断, 经验模态分解, 局部特征尺度分解, 转子碰摩, 总体平均经验模态分解

Abstract: Empirical mode decomposition (EMD), as an adaptive signal decomposition method, has been widely applied to many engineering fields. Two kinds of mean optimized empirical mode decomposition (MOEMD) methods are developed to improve the decomposition performance of EMD. The first method is based on selecting decomposition results with the smallest orthogonality that obtained by different weighted mean curve iteration sifting as the optimal one and the second is selecting the optimal intrinsic mode function in each layer from the results obtained by different weighted mean curve iteration sifting to ensure an overall optimal decomposition result. The two MOEMD algorithms are compared with EMD and other existing signal decomposing methods by analyzing simulation signals and the comparison results indicate that the proposed methods have much more significantly improvement in decomposition performance and precision than EMD and other methods. Finally, the proposed methods are applied to the rotor rubbing fault signal analysis by comparing with EMD and the results show that MOEMD method could effectively identify the rotor rubbing fault and get much better recognition effect than EMD.

Key words: empirical mode decomposition, ensemble empirical mode decomposition, fault diagnosis, local characteristic-scale decomposition, rotor rubbing

中图分类号:

TN911

郑近德, 潘海洋, 程军圣. 均值优化经验模态分解及其在转子故障诊断中的应用[J]. 机械工程学报, 2018, 54(23): 93-101.

ZHENG Jinde, PAN Haiyang, CHENG Junsheng. Mean-optimized Empirical Mode Decomposition and Its Application in Rotor Fault Diagnosis[J]. Journal of Mechanical Engineering, 2018, 54(23): 93-101.

参考文献

[1] 程军圣, 于德介,杨宇. EMD方法在转子局部碰摩故障诊断中的应用[J]. 振动、测试与诊断,2006,26(1):24-27. CHENG Junsheng,YU Dejie,YANG Yu. Application of EMD to local rub-impact fault diagnosis in rotor systems[J]. Journal of Vibration Measurement & Diagnosis,2006,26(1):24-27.
[2] 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[J]. Proceedings of the Royal Society A,1998,454(1971):903-995.
[3] COLOMINAS M A,SCHLOTTHAUER G,TORRES M E. Improved complete ensemble EMD:A suitable tool for biomedical signal processing[J]. Biomedical Signal Processing & Control,2014,14(1):19-29.
[4] LEI Yaguo,LIN Jing,HE Zhengjia,et al. A review on empirical mode decomposition in fault diagnosis of rotating machinery[J]. Mechanical Systems & Signal Processing,2013,35(1-2):108-126.
[5] 郑近德,程军圣. 改进的希尔伯特-黄变换及其在滚动轴承故障诊断中的应用[J]. 机械工程学报,2015,51(1):138-145. ZHENG Jinde,CHENG Junsheng. Improved Hilbert-Huang transform and its applications to rolling bearing fault diagnosis[J]. Journal of Mechanical Engineering,2015,51(1):138-145.
[6] WU Z,HUANG N E. Ensemble empirical mode decomposition:a noise-assisted data analysis method[J]. Advances in Adaptive Data Analysis,2011,1(1):1-41.
[7] YEH J,SHIEH J,HUANG N E. Complementary ensemble empirical mode decomposition:a novel noise enhanced data analysis method[J]. Advances in Adaptive Data Analysis,2011,2(2):135-156.
[8] ZHENG Jinde,CHENG Junsheng,YANG Yu. Partly ensemble empirical mode decomposition:an improved noise-assisted method for eliminating mode mixing[J]. Signal Processing,2014,96:362-374.
[9] 雷亚国. 基于改进Hilbert-Huang变换的机械故障诊断[J]. 机械工程学报,2011,47(5):71-77. LEI Yaguo. Machinery fault diagnosis based on improved Hilbert-Huang transform[J]. Journal of Mechanical Engineering,2011,47(5):71-77.
[10] 雷亚国,孔德同,李乃鹏,等. 自适应总体平均经验模式分解及其在行星齿轮箱故障检测中的应用[J]. 机械工程学报,2014,50(3):64-70. LEI Yaguo,KONG Detong,LI Naipeng,et al. Adaptive ensemble empirical mode decomposition and its application to fault detection of planetary gearboxes[J]. Journal of Mechanical Engineering,2014,50(3):64-70.
[11] SMITH J S. The local mean decomposition and its application to EEG perception data[J]. Journal of the Royal Society Interface,2006,2(5):443-454.
[12] FREI M G,OSORIO I. Intrinsic time-scale decomposition:time-frequency-energy analysis and real-time filtering of non-stationary signals[J]. Proceedings of the Royal Society A Mathematical Physical & Engineering Sciences,2007,463(2078):321-342.
[13] ZHENG Jinde,CHENG Junsheng,YANG Yu. A rolling bearing fault diagnosis approach based on LCD and fuzzy entropy[J]. Mechanism & Machine Theory,2013,70(6):441-453.
[14] 程军圣,郑近德,杨宇. 一种新的非平稳信号分析方法-局部特征尺度分解法[J]. 振动工程学报,2012,25(2):215-220. CHENG Junsheng,ZHENG Jinde,YANG Yu. A nonstationary signal analysis approach-the local characteristic-scale decomposition method[J]. Journal of Vibration Engineering,2012,25(2):215-220.
[15] RILLING G,FLANDRIN P,GONCALVES P. On empirical mode decomposition and its algorithms[C]//Proceedings of the IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing (NSIP 2003),Grado,Italy,June 8-11,2003.
[16] ZHENG J,CHENG J,YANG Y. Generalized empirical mode decomposition and its applications to rolling element bearing fault diagnosis[J]. Mechanical Systems & Signal Processing,2013,40(1):136-153.
[17] 科恩著. 时-频分析:理论与应用[M]. 白居宪译. 西安:西安交通大学出版社,1998. COHEN L. BAI Juxian. Time frequency analysis:Theory and applications[M]. Translated by BAI Juxian. Xi'an:Xi'an Jiao Tong University Press, 1994.
[18] 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. CHENG Junsheng. Research on fault diagnosis of rotating machinery based on Hilbert-Huang transform[D]. Changsha:Hunan University, 2005.

均值优化经验模态分解及其在转子故障诊断中的应用

Mean-optimized Empirical Mode Decomposition and Its Application in Rotor Fault Diagnosis

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	狄子钧, 袁东风, 李东阳, 梁道君, 周晓天, 信苗苗, 曹凤, 雷腾飞. 基于多尺度-高效通道注意力网络的刀具故障诊断方法[J]. 机械工程学报, 2024, 60(6): 82-90.
[2]	张赛凡, 李博, 轩福贞. 激光选区熔化过程声发射信号的降噪与分类预测方法[J]. 机械工程学报, 2024, 60(6): 163-176.
[3]	严如强, 许文纲, 王志颖, 朱启翔, 周峥, 赵志斌, 孙闯, 王诗彬, 陈雪峰, 张军辉, 徐兵. 航空发动机燃油控制系统故障诊断技术研究进展与挑战[J]. 机械工程学报, 2024, 60(4): 3-31.
[4]	张军辉, 刘施镐, 徐兵, 黄伟迪, 吕飞, 黄晓琛. 轴向柱塞泵智能化关键技术研究进展及发展趋势[J]. 机械工程学报, 2024, 60(4): 32-49.
[5]	丁孺琦, 熊文杰, 程敏, 徐兵. 智能化阀口独立电液控制系统安全性能评估[J]. 机械工程学报, 2024, 60(4): 101-112.
[6]	王志颖, 李天福, 许文纲, 孙闯, 张军辉, 徐兵, 严如强. 降噪混合注意力变分自编码器及其在轴向柱塞泵故障诊断中的应用[J]. 机械工程学报, 2024, 60(4): 167-177.
[7]	邵海东, 林健, 闵志闪, 明宇航. 分布外样本干扰下基于改进半监督原型网络的齿轮箱跨域故障诊断[J]. 机械工程学报, 2024, 60(4): 212-221.
[8]	潘海洋, 徐海锋, 郑近德, 童靳于, 张飞斌. 基于双加权不平衡矩阵分类器的机械故障诊断方法[J]. 机械工程学报, 2024, 60(3): 170-180.
[9]	赵轲, 叶敏, 王瑞欣, 陆海, 刘孟孟, 邵海东. 基于模糊域自适应的源自由域旋转机械故障诊断方法[J]. 机械工程学报, 2024, 60(18): 43-52.
[10]	李志鹏, 马天雨, 刘金平, 向青松, 唐俊杰. 基于非对称性对抗训练的多源域自适应智能故障诊断方法[J]. 机械工程学报, 2024, 60(18): 76-88.
[11]	汪煜坤, 易彩, 汪浩, 周秋阳, 冉乐, 王靖元. PSD引导的自适应频带划分方法及其在轴承故障诊断中的应用[J]. 机械工程学报, 2024, 60(17): 179-193.
[12]	刘一龙, 李心远, 陈银萍, 成玮, 陈雪峰. 基于峭度曲面极大值的电机轴承保持架故障诊断方法[J]. 机械工程学报, 2024, 60(15): 89-99.
[13]	于小洛, 杨阳, 杜明刚, 何清波, 彭志科. 机械传动系统含偏差频率分量协同检测与分解方法[J]. 机械工程学报, 2024, 60(15): 100-112.
[14]	黄包裕, 张永祥. 一种基于三步法的轴承故障诊断方法[J]. 机械工程学报, 2024, 60(14): 51-68.
[15]	刘征宇, 张政, 郭乐凯, 孟辉, 刘项. 基于经验模态分解的锂离子电池健康状态预测[J]. 机械工程学报, 2024, 60(14): 272-281.