改进的正弦辅助多元经验模式分解及其在滚动轴承故障诊断中的应用

doi:10.3969/j.issn.1004-132X.2022.11.010

摘要/Abstract

摘要： 正弦辅助多元经验模式分解算法（SA-MEMD）通过在额外的通道中加入正弦辅助信号来减少模式混合，但该算法对噪声敏感，辅助信号的主频率比需要根据经验确定，为此，提出了一种改进的正弦辅助多元经验模式分解算法。首先使用非局部均值降噪对原始信号进行预处理，减少噪声对算法的干扰，其次使用短时傅里叶变换确定信号频谱范围，然后以最小集成EMD能量熵准则选择最优主频率比，最后根据正弦辅助多元经验模式分解算法的步骤进行信号处理。模拟信号和实际信号的对比分析结果证明，改进的方法可以减少传统的多元经验模式分解方法存在的模式混合现象。

关键词: 故障诊断, 正弦辅助多元经验模式分解, 模式混合, 短时傅里叶变换, 能量熵

Abstract: The SA-MEMD algorithm reduced mode mixing by adding a sine assisted signal to an additional channel, but the algorithm was sensitive to noises, and the main frequency ratio of the auxiliary signals needed to be determined empirically. For this reason, an improved SA-MEMD was proposed. First, The non-local mean noise reduction was used to preprocess the original signals to reduce noise interference for the algorithm, then the short-time Fourier transform was used to determine the signal spectrum range, thus the optimal main frequency ratio was selected based on the minimum ensemble EMD energy entropy criterion. The steps of the SA-MEMD algorithm were used to complete signal processing. The analysis of simulation signals and actual signals proves that the improved method may alleviate the mode mixing phenomenon that exists in the traditional multivariate empirical mode decomposition methods.

Key words: fault diagnosis, sine-assisted multivariate empirical mode decomposition(SA-MEMD), modal aliasing, short-time Fourier transform, energy entropy

中图分类号:

吴利锋, 吕勇, 袁锐, 朱熹, 游俊, . 改进的正弦辅助多元经验模式分解及其在滚动轴承故障诊断中的应用[J]. 中国机械工程, 2022, 33(11): 1336-1344.

WU Lifeng, , LYU Yong, YUAN Rui, ZHU Xi, YOU Jun, . Improved SA-MEMD with Applications to Fault Diagnosis of Rolling Bearings[J]. China Mechanical Engineering, 2022, 33(11): 1336-1344.

参考文献

［1］马宏伟，张大伟，曹现刚，等. 基于EMD的振动信号去噪方法研究［J］. 振动与冲击， 2016， 35(22):38-40.
MA Hongwei, ZHANG Dawei, CAO Xiangang, et al. Research on De-noising Method of Vibration Signal Based on EMD［J］. Vibration and Shock, 2016,35(22):38-40.
［2］张淑清, 黄文静, 胡永涛,等. 基于总体平均经验模式分解近似熵和混合PSO-BP算法的轴承故障诊断方法［J］. 中国机械工程, 2016, 27(22):3048-3054.
ZHANG Shuqing, HUANG Wenjing, HU Yongtao, et al. Bearing Fault Diagnosis Method Based on Overall Average Empirical Mode Decomposition Approximate Entropy and Hybrid PSO-BP Algorithm［J］. China Mechanical Engineering, 2016, 27(22):3048-3054.
［3］张迅, 赵宇, 阮灵辉,等. 基于小波变换分析箱梁振动噪声的时频特性［J］. 西南交通大学学报, 2020, 55，251(1):113-121.
ZHANG Xun, ZHAO Yu, RUAN Linghui, et al. Analysis of the Time-frequency Characteristics of Box Girder Vibration and Noise Based on Wavelet Transform［J］. Journal of Southwest Jiaotong University, 2020, 55，251(1):113-121 .
［4］卞家磊, 朱春梅, 蒋章雷,等. LMD-ICA联合降噪方法在滚动轴承故障诊断中的应用［J］. 中国机械工程, 2016, 27(7):904-910.
BIAN Jialei, ZHU Chunmei, JIANG Zhanglei, et al. Application of LMD-ICA Joint Noise Reduction Method in Rolling Bearing Fault Diagnosis［J］. China Mechanical Engineering, 2016, 27(7):904-910.
［5］马洪斌, 佟庆彬, 张亚男. 优化参数的变分模态分解在滚动轴承故障诊断中的应用［J］. 中国机械工程, 2018, 29(4):390-397.
MA Hongbin, TONG Qingbin, ZHANG Yanan. Application of Variational Modal Decomposition of Optimal Parameters in Fault Diagnosis of Rolling Bearings［J］. China Mechanical Engineering, 2018, 29(4):390-397.
［6］胡茑庆, 陈徽鹏, 程哲,等. 基于经验模态分解和深度卷积神经网络的行星齿轮箱故障诊断方法［J］. 机械工程学报, 2020， 55（7）：9-18.
HU Niaoqing, CHEN Huipeng, CHENG Zhe, et al. Fault Diagnosis Method of Planetary Gearbox Based on Empirical Mode Decomposition and Deep Convolutional Neural Network［J］. Chinese Journal of MechanicalEngineering, 2020， 55（7）：9-18.
［7］时培明, 蒋金水, 刘彬,等. 基于边界特征尺度匹配延拓的EMD改进方法及应用［J］. 中国机械工程, 2014, 25(12):1616-1623.
SHI Peiming, JIANG Jinshui, LIU Bin, et al. Improved Method and Application of EMD Based on Boundary Feature Scale Matching and Continuation［J］. China Mechanical Engineering, 2014, 25(12):1616-1623.
［8］田海雷，李洪儒，许葆华. 基于集总经验模式分解和支持向量机的液压泵故障预测研究［J］. 中国机械工程，2013，24(7):926-931.
TIAN Hailei, LI Hongru, XU Baohua. Research on Hydraulic Pump Fault Prediction Based on Lumped Empirical Mode Decomposition and Support Vector Machine［J］. China Mechanical Engineering, 2013, 24(7):926-931.
［9］周涛涛，朱显明，彭伟才，等. 基于CEEMD和排列熵的故障数据小波阈值降噪方法［J］. 振动与冲击，2015， 34（23）：207-211.
ZHOU Taotao, ZHU Xianming, PENG Weicai, et al. Wavelet Threshold Noise Reduction Method for Fault Data Based on CEEMD and Permutation Entropy［J］. Vibration and Shock, 2015， 34（23）：207-211.
［10］LYU Y, YUAN R, WANG T, et al. Health Degradation Monitoring and Early Fault Diagnosis of a Rolling Bearing Based on CEEMDAN and Improved MMSE［J］. Materials, 2018, 11(6):1009-1031.
［11］REHMAN N, MANDIC D P. Multivariate Empi-rical Mode Decomposition［J］. Proceedings of the Royal Society A:Mathematical, Physical and Engineering Sciences, 2010， 466：1291-1302.
［12］张淑清, 胡永涛, 李盼,等. 基于MEMD互近似熵及FCM聚类的轴承故障诊断方法［J］. 中国机械工程, 2015, 26(19):2613-2618.
ZHANG Shuqing, HU Yongtao, LI Pan, et al. Bearing Fault Diagnosis Method Based on MEMD Mutual Approximate Entropy and FCM Clustering［J］. China Mechanical Engineering, 2015, 26(19):2613-2618.
［13］李亚兰, 金炜东. 全矢样本熵在高速列车故障诊断中的应用［J］. 振动.测试与诊断, 2020,40(4):794-799.
LI Yalan, JIN Weidong. The Application of Total Vector Sample Entropy in the Fault Diagnosis of High-speed Trains［J］. Journal of Vibration， Test & Diagnosis, 2020,40(4):794-799.
［14］武哲, 杨绍普, 任彬,等. 基于噪声辅助多元经验模态分解和多尺度形态学的滚动轴承故障诊断方法［J］. 振动与冲击, 2016, 35(4):127-133.
WU Zhe, YANG Shaopu, REN Bin, et al. Rolling Bearing Fault Diagnosis Method Based on Noise-assisted Multivariate Empirical Mode Decomposition and Multi-scale Morphology［J］. Journal of Vibration and Shock, 2016, 35(4):127-133.
［15］GE S, SHI Y,WANG R, et al. Sinusoidal Signal Assisted Multivariate Empirical Mode Decomposition for Brain-computer Interfaces［J］. IEEE Journal of Biomedical and Health Informatics, 2018.
［16］LAD B V, NEOLE B A,BHURCHANDI K M. Digital Image Restoration Using NL Means with Robust Edge Preservation Technique［C］∥International Conference on ISMAC in Computational Vision and Bio-engineering. Cham, 2018:763-774.
［17］LYU Y, YUAN R, et al. Multivariate Empirical Mode Decomposition and Its Application to Fault Diagnosis of Rolling Bearing［J］. Mechanical Systems & Signal Processing, 2016, 81:219-234.
［18］陈剑, 杨斌, 黄凯旋,等. 一种脊线提取方法在轴承故障诊断中的应用［J］. 中国机械工程, 2021,32(10):1157-1163.
CHEN Jian, YANG Bin, HUANG Kaixuan, et al. Application of a Ridgeline Extraction Method in Bearing Fault Diagnosis［J］. China Mechanical Engineering,2021,32(10):1157-1163.
［19］YUAN R, LYU Y, LI H, et al. Robust Fault Diagnosis of Rolling Bearings Using Multivariate Intrinsic Multiscale Entropy Analysis and Neural Network under Varying Operating Conditions［J］. IEEE Access, 2019, 7:130804-130819.
［20］FAURE H, PILLICHSHAMMER F. LP Discrepancy of Generalized Two-dimensional Hammersley Point Sets［J］. Monatshefte für Mathematik, 2009, 158(1):31-61.
［21］马艳丽, 金兵, 张学欣,等. 基于全矢融合与多维经验模态分解的滚动轴承退化过程频谱结构研究［J］. 中国机械工程, 2017, 28(14):1747-1752.
MA Yanli, JIN Bing, ZHANG Xuexin, et al. Research on Spectrum Structure of Rolling Bearing Degradation Process Based on Full Vector Fusion and Multi-dimensional Empirical Mode Decomposition［J］. China Mechanical Engineering, 2017, 28(14):1747-1752.
［22］张超，陈建军，郭迅. 基于EMD能量熵和支持向量机的齿轮故障诊断方法［J］. 振动与冲击，2012，29(10):216-220.
ZHANG Chao, CHEN Jianjun, GUO Xun. Gear Fault Diagnosis Method Based on EMD Energy Entropy and Support Vector Machine［J］. Journal of Vibration and Shock, 2012, 29(10):216-220.
［23］张超，陈建军，郭迅. 基于EEMD能量熵和支持向量机的齿轮故障诊断方法［J］. 中南大学学报:自然科学版，2012， 43（3）：932-939.
ZHANG Chao, CHEN Jianjun, GUO Xun. Gear Fault Diagnosis Method Based on EEMD Energy Entropy and Support Vector Machine［J］. Journal of Central South University:Natural Science Edition, 2012， 43（3）：932-939.
［24］HU W,MO J. Improvement of Frequency Resolution of EMD Using an Optimized Masking Signal［C］∥2010 International Conference on Multimedia Technology. 2010:1-4.
［25］HUANG S, WANG X, LI C, et al. Data Decomposition Method Combining Permutation Entropy and Spectral Substitution with Ensemble Empirical Mode Decomposition［J］. Measurement, 2019， 139：438-453.

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