自适应掩膜信号集成局部特征尺度分解及其应用

doi:10.3969/j.issn.0372-2112.2020.10.025

电子学报 ›› 2020, Vol. 48 ›› Issue (10): 2060-2070.DOI: 10.3969/j.issn.0372-2112.2020.10.025

自适应掩膜信号集成局部特征尺度分解及其应用

郑近德¹, 潘海洋¹, 童靳于¹, 刘庆运¹, 丁克勤²

1. 安徽工业大学机械工程学院, 安徽马鞍山 243032;
2. 中国特种设备检测研究院, 北京 100029

收稿日期:2019-05-05 修回日期:2020-04-30 出版日期:2020-10-25
- 作者简介:
- 郑近德 男,1986年生于安徽阜阳.博士,现为安徽工业大学机械工程学院副教授.研究方向为动态信号处理、时频分析及机械设备故障诊断,已发表学术论文40余篇.E-mail:lqdlzheng@126.com
  潘海洋 男,1989年生于安徽宿州.硕士,现为安徽工业大学机械工程学院助教.研究方向为模式识别与机械设备故障诊断.E-mail:pansea@sina.cn
  童靳于 女,1987年生于安徽淮南.硕士,现为安徽工业大学机械工程学院实验师.研究方向为统计信号处理、振动信号分析与测量.E-mail:pantc2006@163.com
- 基金资助:
- 国家重点研发计划 (No.2017YFC0805100）; 国家自然科学基金 (No.51975004）; 安徽省高校自然科学研究重点项目 (No.KJ2019A0053，No.KJ2019A092）

Adaptive Mask Signal-Based Local Characteristic-Scale Decomposition and Its Application

ZHENG Jin-de¹, PAN Hai-yang¹, TONG Jin-yu¹, LIU Qing-yun¹, DING Ke-qin²

1. School of Mechanical Engineering, Anhui University of Technology, Maanshan, Anhui 243032, China;
2. China Special Equipment Inspection and Research Institute, Beijing 100029, China

Received:2019-05-05 Revised:2020-04-30 Online:2020-10-25 Published:2020-10-25
- Supported by:
- National Key Research and Development Program of China (No.2017YFC0805100); National Natural Science Foundation of China (No.51975004); Key Program of Natural Science Research Projrct of Colleges and Universities of Anhui Province (No.KJ2019A0053, No.KJ2019A092)

摘要/Abstract

摘要： 局部特征尺度分解（LCD）是为克服经验模态分解（EMD）中均值曲线构造的不足而提出的一种自适应信号分解方法，已被应用于机械故障诊断领域.但LCD存在与EMD类似的模态混叠问题，为此，基于均匀相位差掩膜信号构造，提出了自适应掩膜信号集成局部特征尺度分解（AMSELCD），该方法不仅能够将一个复杂信号自适应地分解为若干个本征模态函数和一个剩余项之和，而且能够有效地解决LCD的模态混叠现象.通过仿真信号分析，将AMSELCD与现有多种抑制模态分解方法进行了对比，结果表明了所提方法的有效性和优越性.最后，针对滚动轴承和转子碰摩故障振动信号的调制特征，将所提AMSELCD方法应用于转子碰摩和滚动轴承的故障诊断，对比和实验分析结果进一步验证了所提方法的有效性和优越性.

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

Abstract: Local characteristic-scale decomposition (LCD) is an adaptive signal decomposition method proposed to overcome the shortcomings of mean curve construction in empirical mode decomposition (EMD) and has been applied to mechanical fault diagnosis. However, LCD also has the mode mixing problem that exists in EMD. Based on the construction of masking signal with uniform phase difference, the adaptive mask signal ensemble local characteristic-scale decomposition (AMSELCD) is proposed in this paper to adaptively decompose a complex signal into several intrinsic mode functions and a residue, which can effectively alleviate the mode mixing phenomenon of LCD. AMSELCD is compared with various existing methods for restraining mode mixing through simulation signal analysis and the results show the effectiveness and superiority of the proposed method. Finally, aiming at the modulation characteristics of fault signals of rolling bearing and rotor rubbing, the proposed AMSELCD method is applied to the fault diagnosis of rotor rubbing and rolling bearing, and the experimental comparison analysis results further verify the effectiveness and superiority of AMSELCD.

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

中图分类号:

TN911.7

郑近德, 潘海洋, 童靳于, 等. 自适应掩膜信号集成局部特征尺度分解及其应用[J]. 电子学报, 2020, 48(10): 2060-2070.

ZHENG Jin-de, PAN Hai-yang, TONG Jin-yu, et al. Adaptive Mask Signal-Based Local Characteristic-Scale Decomposition and Its Application[J]. Acta Electronica Sinica, 2020, 48(10): 2060-2070.

参考文献

[1] LEI Y,LIN J,HE Z,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.
[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] 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 Jin-de,CHENG Jun-sheng.Improved Hilbert-huang transform and its applications to rolling bearing fault diagnosis[J].Journal of Mechanical Engineering,2015,51(1):138-145.(in Chinese)
[4] TONG W,ZHANG M,YU Q,et al.Comparing the applications of EMD and EEMD on time-frequency analysis of seismic signal[J].Journal of Applied Geophysics,2012,83:29-34.
[5] 郑近德,潘海洋,张俊,等.APEEMD及其在转子碰摩故障诊断中的应用[J].振动、测试与诊断,2016,(2):257-263. ZHENG Jin-de,PAN Hai-yang,ZHANG Jun,et al.APEEMD and its application to rotor rubbing fault diagnosis[J].Journal of Vibration Measurement and Diagnosis,2016,(2):257-263.(in Chinese)
[6] 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 Ya-guo,KONG De-tong,LI Nai-peng,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.(in Chinese)
[7] 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 Ya-guo.Improved Hilbert-Huang transform based mechanical fault diagnosis[J].Journal of Mechanical Engineering,2011,47(5):71-77.(in Chinese)
[8] XIN Z,LIU Z,QIANG M,et al.An optimized time varying filtering based empirical mode decomposition method with grey wolf optimizer for machinery fault diagnosis[J].Journal of Sound Vibration,2018,418:55-78.
[9] 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.
[10] 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.
[11] FENG Z,LIN X,ZUO M-J.Joint amplitude and frequency demodulation analysis based on intrinsic time-scale decomposition for planetary gearbox fault diagnosis[J].Mechanical Systems & Signal Processing,2016,72-73:223-240.
[12] 郑近德,程军圣,曾鸣.基于改进的局部特征尺度分解和归一化正交的时频分析方法[J].电子学报,2015,43(7):1418-1424. ZHENG Jin-de,CHENG Jun-sheng,ZENG Ming.A new time frequency analysis method based on improved local characteristic-scale decomposition and normalized quadrature[J].Acta Electronica Sinica,2015,43(7):1418-1424.(in Chinese)
[13] WANG Y,HE Z,ZI Y.A demodulation method based on improved local mean decomposition and its application in rub-impact fault diagnosis[J].Measurementence & Technology,2009,20(2):025704.
[14] YU Y,CHENG J,KANG Z.An ensemble local means decomposition method and its application to local rub-impact fault diagnosis of the rotor systems[J].Measurement,2012,45(3):561-570.
[15] 程军圣,郑近德,杨宇,等.基于部分集成局部特征尺度分解与拉普拉斯分值的滚动轴承故障诊断模型[J].振动工程学报,2014,27(6):942-950. CHENG Jun-sheng,ZHENG Jin-de,YANG Yu,et al.Fault diagnosis model for rolling bearing based on partly ensemble local characteristic-scale decomposition and Laplacian score[J].Journal of Vibration Engineering,2014,27(6):942-950.(in Chinese)
[16] 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 Jun-sheng,ZHENG Jin-de,YANG Yu.A new stationary signal analysis method-local Characteristic-scale decomposition method[J].Journal of Vibration Engineering,2012,25(2):215-220.(in Chinese)
[17] Prince A,Senroy N,Balasubramanian R.Targeted approach to apply masking signal-based empirical mode decomposition for mode identification from dynamic power system wide area measurement signal data[J].IET Generation Transmission & Distribution,2011,5(10):1025-1032.
[18] WU Z,HUANG N.Ensemble empirical mode decomposition:a noise-assisted data analysis method[J].Advances in Adaptive Data Analysis,2011,1(01):1-41.
[19] Yeh J,Shieh J,Huang N.Complementary ensemble empirical mode decomposition:a novel noise enhanced data analysis method[J].Advances in Adaptive Data Analysis,2011,2(02):135-156.
[20] Imaouchen Y,Kedadouche M,Alkama R,et al.A Frequency-weighted energy operator and complementary ensemble empirical mode decomposition for bearing fault detection[J].Mechanical Systems & Signal Processing,2017,82:103-116.
[21] Torres M E,Colominas M A,Schlotthauer G,et al.A complete ensemble empirical mode decomposition with adaptive noise[A].IEEE International Conference on Acoustics[C].USA:IEEE,2011.DOI:10.1109/ICASSP.2011.5947265.
[22] 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.
[23] 郑近德,程军圣,杨宇.部分集成局部特征尺度分解:一种新的基于噪声辅助数据分析方法[J].电子学报,2013,41(5):1030-1035. ZHENG Jin-de,CHENG Jun-sheng,YANG Yu.Partly ensemble local characteristic-scale decomposition:A new noise assisted data analysis method[J].Acta Electronica Sinica,2013,41(5):1030-1035.(in Chinese)
[24] Wang Yung-Hung,Hu Kun,Lo Men-Tzung.Uniform phase empirical mode decomposition:An optimal hybridization of masking signal and ensemble approaches[J].IEEE Access,2018,6:34819-34833.
[25] 郑近德,程军圣,杨宇.多尺度排列熵及其在滚动轴承故障诊断中的应用[J].中国机械工程,2013,24(19):2641-2646. ZHENG Jin-de,CHENG Jun-sheng,YANG Yu.Multi-scale permutation entropy and its applications to rolling bearing fault diagnosis[J].China Mechanical Engineering,2013,24(19):2641-2646.(in Chinese)
[26] Rilling G,Flandrin P,Goncalves P.On empirical mode decomposition and its algorithms[A].Proceedings of IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing NSIP-03[C].USA:IEEE,2003.1-5.
[27] Smith W A,Randall R B.Rolling element bearing diagnostics using the case western reserve university data:A benchmark study[J].Mechanical Systems & Signal Processing,2015,64-65:100-131.
[28] Sawalhi N,Randall R B.Signal pre-whitening for fault detection enhancement and surveillance in rolling element bearings[A].The Eighth International Conference on Condition Monitoring and Machinery Failure Prevention Technologies[C].Cardiff,UK:Curran Associates,Inc,2011.549-561.
[29] Jinde Zheng,Junsheng Cheng,Yu Yang.Partly ensemble empirical mode decomposition:An improved noise-assisted method for eliminating mode mixing[J].Signal Processing,2014,96(Part B):362-374.

自适应掩膜信号集成局部特征尺度分解及其应用

Adaptive Mask Signal-Based Local Characteristic-Scale Decomposition and Its Application

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

[1]	曹源, 宋迪, 胡小溪, 孙永奎. 基于改进时域多尺度散布熵与支持向量机的转辙机故障诊断[J]. 电子学报, 2023, 51(1): 117-127.
[2]	于建立, 吴传斌, 冷赫, 鲁志伟. 基于磁场分布特性的接地网故障诊断研究[J]. 电子学报, 2022, 50(3): 598-607.
[3]	欧阳丹彤, 刘扬, 宋金彩, 王浩然, 张立明. 结合结构特征基于测试集重排序的故障诊断方法[J]. 电子学报, 2022, 50(1): 63-71.
[4]	罗远兴, 李志红, 梁兴, 李超, 胡凤城. 基于EMD‑LS的非平稳时间序列多重分形去趋势波动分析方法[J]. 电子学报, 2021, 49(12): 2323-2329.
[5]	鲍中新, 文成林, 马雪. 一种基于数据变化率的预处理及主元分析故障诊断方法[J]. 电子学报, 2021, 49(11): 2234-2240.
[6]	张彩霞, 王子涵, 文成林, 刘国文, 余伟. 样本空间基于多级高维特征表示的微小故障诊断[J]. 电子学报, 2020, 48(8): 1647-1654.
[7]	石鹏, 张启忠, 张华平, 席旭刚. 基于多元经验模态分解的多元多尺度熵静态平衡能力评估[J]. 电子学报, 2020, 48(4): 670-674.
[8]	郑近德, 潘海洋, 戚晓利, 张兴权, 刘庆运. 基于改进经验小波变换的时频分析方法及其在滚动轴承故障诊断中的应用[J]. 电子学报, 2018, 46(2): 358-364.
[9]	刘毅, 宋余庆, 刘哲. 基于有理四次Hermite插值和PSO的EMD包络线拟合算法[J]. 电子学报, 2018, 46(11): 2761-2767.
[10]	李应, 印佳丽. 基于多随机森林的低信噪比声音事件检测[J]. 电子学报, 2018, 46(11): 2705-2713.
[11]	苏维均, 杨飞, 于重重, 程晓卿, 崔世杰. 基于局部频谱的滚动轴承故障特征提取方法[J]. 电子学报, 2018, 46(1): 160-166.
[12]	潘海洋, 郑近德, 杨宇, 童宝宏. 基于CELCD和MFVPMCD的智能故障诊断方法研究[J]. 电子学报, 2017, 45(3): 546-551.
[13]	刘荣胜, 彭敏放, 肖祥慧. 基于谱聚类集成的变压器在线故障诊断[J]. 电子学报, 2017, 45(10): 2491-2497.
[14]	张军, 潘泽鑫, 郑玉新, 李言. 振动信号趋势项提取方法研究[J]. 电子学报, 2017, 45(1): 22-28.
[15]	郑近德, 潘海洋, 程军圣. 非平稳信号分析的广义解析模态分解方法[J]. 电子学报, 2016, 44(6): 1458-1464.