基于EEMD的多尺度模糊熵的齿轮故障诊断

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振动与冲击 ›› 2015, Vol. 34 ›› Issue (14) : 163-167.

论文

杨望灿，张培林，王怀光，陈彦龙，孙也尊

作者信息 +

Gear fault diagnosis based on multiscale fuzzy entropy of EEMD

YANG Wang-can，ZHANG Pei-lin，WANG Huai-guang，CHEN Yan-long,SUN Ye-zun

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文章历史 +

摘要

为准确利用振动信号进行故障诊断，提出基于EEMD多尺度模糊熵的齿轮故障诊断方法。利用集合经验模态分解（EEMD）对振动信号进行自适应分解，获得原始信号的不同尺度分量；据模糊熵能有效区分不同信号的复杂度，计算EEMD分解所得本征模态函数（IMF）分量模糊熵，获得原始信号多个尺度的复杂测度作为齿轮不同状态的特征参数；将该特征参数输入最小二乘支持向量机（LS-SVM）分类器判断齿轮故障。齿轮箱齿轮故障实验结果表明，该方法能提高齿轮故障诊断精度。

Abstract

In order to diagnose fault accurately by using vibration signal, a method of gear fault diagnosis based on multiscale fuzzy entropy of EEMD was proposed. Firstly, the vibration signal was decomposed adaptively with ensemble empirical mode decomposition (EEMD) and then the components in different scales of original signal were gained. Because the fuzzy entropy could distinguish the complexity of different signals effectively, the fuzzy entropy of intrinsic mode functions (IMFs) by EEMD was calculated. Thus the complexity metric in different scales of original signal was gained, which could be the feature parameter that described the different states of gear. At last the feature parameters were put into least square support vector machine (LS-SVM) for diagnosing the gear fault. Results of the gear box fault test indicated that the proposed method diagnosed gear fault with high accuracy.

导出引用

杨望灿，张培林，王怀光，陈彦龙，孙也尊. 基于EEMD的多尺度模糊熵的齿轮故障诊断[J]. 振动与冲击, 2015, 34(14): 163-167

YANG Wang-can，ZHANG Pei-lin，WANG Huai-guang，CHEN Yan-long,SUN Ye-zun. Gear fault diagnosis based on multiscale fuzzy entropy of EEMD[J]. Journal of Vibration and Shock, 2015, 34(14): 163-167

参考文献

[1] 何正嘉,陈进,王太勇,等. 机械故障诊断理论及应用[M]. 北京:高等教育出版社, 2010.
[2] 林近山,陈前. 基于非平稳时间序列双标度指数特征的齿轮箱故障诊断[J]. 机械工程学报,2012,48(13):108- 114.
LIN Jin-shan, CHEN Qian. Fault diagnosis of gearboxes based on the double-scaling-exponent characteristic of nonstationary time series[J]. Journal of Mechanical Engineering, 2012,48 (13): 108-114.
[3] 邵忍平,曹精明,李永龙. 基于EMD小波阈值去噪和时频分析的齿轮故障模式识别与诊断[J]. 振动与冲击, 2012,31 (8): 96-101.
SHAO Ren-ping, CAO Jing-ming, LI Yong-long. Gear
fault pattern identification and diagnosis using time- frequency analysis and wavelet threshold de-noising based on EMD[J]. Journal of Vibration and Shock, 2012,31 (8): 96-101.
[4] Lei Y G, Zuo M J, He Z J, et al. A multidimensional hybrid intelligent method for gear fault diagnosis[J]. Expert Systems with Applications, 2010,37: 1419-1430.
[5] 王凤利,段树林,于洪亮,等. 基于EEMD和形态学分形维数的柴油机故障诊断[J]. 内燃机学报,2012,30 (3): 557-562.
WANG Feng-li, DUAN Shu-lin, YU Hong-liang, et al. Fault diagnosis of diesel engine based on eemd and morphology fractal dimension[J]. Transactions of CSICE, 2012,30 (3): 557-562.
[6] 庄建军,宁新宝,邹鸣,等. 两种熵测度在量化射击运动员短时心率变异性信号复杂度上的一致性[J]. 物理学报, 2008,57 (5): 2805-2811.
ZHUANG Jian-jun, NING Xin-bao, ZOU Ming, et al. Agreement of two entropy based measures on quantifying the complexity of short term heart rate variability signals from professional shooters[J]. Acta Physica Sinica, 2008,57 (5): 2805-2811.
[7] 赵志宏,杨绍普. 基于小波包变换与样本熵的滚动轴承故障诊断[J]. 振动、测试与诊断, 2012,32 (4): 640-644.
ZHAO Zhi-hong, YANG Shao-pu. Roller bearing fault diagnosis based on wavelet packet transform and sample entropy[J]. Journal of Vibration, Measurement & Diagnosis, 2012,32 (4): 640-644.
[8] Yentes J M, Hunt N, Schmid K K, et al. The appropriate use of approximate entropy and sample entropy with short data sets[J]. Annals of Biomedical Engineering, 2013,41 (2): 349-365.
[9] Pincus S M. Approximate entropy as a measure of system complexity[J]. Proceedings of the National Academy of Sciences USA, 1991,88: 2297-2301.
[10] Richman J S, Moorman J R. Physiological time series analysis using approximate entropy and sample entropy[J]. American Journal of Physiology-Heart and Circulatory Physiology, 2000,278 (6): 2039-2049.
[11] Chen W, Wang Z, Xie H, et al. Characterization of surface EMG signal based on fuzzy entropy[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2007,15 (2): 266-272.
[12] Chen Wei-ting, Zhuang Jun, Yu Wang-xin, et al. Measuring complexity using FuzzyEn, ApEn, and SampEn[J]. Medical Engineering and Physics, 2009,
31 (1): 61-68.
[13] 郑近德,程军圣,杨宇. 基于多尺度熵的滚动轴承故障诊断方法[J]. 湖南大学学报（自然科学版）, 2012,39 (5): 38-41.
ZHENG Jin-de, CHENG Jun-sheng, YANG Yu. A rolling bearing fault diagnosis approach based on multiscale entropy.[J]. Journal of Hunan University (Natural Sciences), 2012,39 (5): 38-41.
[14] 陈仁祥,汤宝平,马婧华. 基于EEMD的振动信号自适应降噪方法[J]. 振动与冲击, 2012,31 (15): 82-86.
CHEN Ren-xiang, TANG Bao-ping, MA Jing-hua. Adaptive de-noising method based on ensemble empirical mode decomposition for vibration signal[J]. Journal of Vibration and Shock, 2012,31 (15): 82-86.
[15] 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. Machinery fault diagnosis based on improved Hilbert-Huang transform[J] Journal of Mechanical Engineering, 2011,47 (5): 71-77.