滚动轴承自适应特征提取的包络谱多点峭度多级降噪方法

doi:10.3969/j.issn.1004-132X.2021.24.007

中国机械工程 ›› 2021, Vol. 32 ›› Issue (24): 2950-2959.DOI: 10.3969/j.issn.1004-132X.2021.24.007

滚动轴承自适应特征提取的包络谱多点峭度多级降噪方法

张龙1;蔡秉桓1;熊国良1;王朝兵1,2;胡俊锋3

1.华东交通大学机电与车辆工程学院，南昌，330013
2.中车戚墅堰机车有限公司，常州，213011
3.中国铁路南昌局集团有限公司科学技术研究所，南昌，330002

出版日期:2021-12-25 发布日期:2022-01-11
作者简介:张龙，男，1980年生，博士、副教授。研究方向为故障诊断、工程信号处理及智能算法。E-mail：longzh@126.com。
基金资助:
国家自然科学基金（51665013，51865010）；
江西省教育厅科学技术研究项目（191327）；
江西省自然科学基金（20212BAB204007，20171BAB216030）；
江西省研究生创新资金（YC2018-S248, YC2019-S243）

Multi-stage Noise Reduction Method with ESMK for Adaptive Feature Extraction of Rolling Bearings

ZHANG Long1;CAI Binghuan1;XIONG Guoliang1;WANG Chaobing1,2;HU Junfeng3

1.School of Mechatronics ＆ Vehicle Engineering, East China Jiaotong University，Nanchang，330013
2.CRRC Qishuyan Co., Ltd.，Changzhou，Jiangsu,213011
3.Institute of Science and Technology,China Railway Nanchang Railway Group Co.,Ltd.，Nanchang,330002

Online:2021-12-25 Published:2022-01-11

摘要/Abstract

摘要： 滚动轴承故障信号主要包含高品质因子振动分量和低品质因子瞬态冲击分量。采用多点最优最小熵解卷积方法初步削弱传输路径等干扰影响，使微弱瞬态冲击成分得到初步增强,然后针对共振稀疏分解(RSSD)方法存在的品质因子选择困难问题，同时考虑包络谱中故障频率成分的严格周期性，提出包络谱多点峭度(ESMK)概念并将其作为优化指标，采用粒子群优化算法(PSO)对品质因子进行选择，得到一种自适应稀疏分解方法（PSO-RSSD）用于瞬态冲击信号的提取，以消除信号中高幅值干扰冲击和背景噪声的影响。轴承仿真与实测信号分析结果表明，与最小熵解卷积信号共振稀疏分解方法相比，在强冲击干扰下ESMK能够有效度量周期性瞬态冲击，PSO-RSSD方法能自适应分离最优低品质共振分量，验证了该方法的有效性和优越性。

关键词: 包络谱多点峭度（ESMK）, 特征提取, 稀疏分解, 粒子群优化, 解卷积

Abstract: The rolling bearing fault signals contained both of the high quality factor oscillation components and low quality factor periodic transient impact components. The method of multipoint optimal minimum entropy deconvolution adjusted(MOMEDA) was adopted to weaken the influences of interferences such as transmission path firstly, and the weak transient impact components were enhanced initially. Furthermore, the problems of quality factor determination in resonance-based signal sparse decomposition(RSSD) method and the strict periodicity of fault frequency components in the envelope spectrum were considered and the PSO was used to optimize the quality factors based on proposed novel index called envelope spectrum multi-point kurtosis(ESMK). In consequence, a new self-adaptive transient impact extraction method called PSO-RSSD was proposed, which might effectively eliminate the impacts of high amplitude interference and background noises. Bearing simulation and measured signal analysis results show that, compared with minimum entropy deconvolution(MED)-RSSD method, ESMK may effectively measure periodic transient impacts under strong impact interference, and PSO-RSSD may separate adaptively the optimal low quality resonance components, which verifies the effectiveness and superiority of the proposed method.

Key words: envelope spectrum multi-point kurtosis(ESMK), feature extraction, sparse decomposition, particle swarm optimization(PSO), deconvolution

中图分类号:

TH133.3

张龙, 蔡秉桓, 熊国良, 王朝兵, 胡俊锋. 滚动轴承自适应特征提取的包络谱多点峭度多级降噪方法[J]. 中国机械工程, 2021, 32(24): 2950-2959.

ZHANG Long, CAI Binghuan, XIONG Guoliang, WANG Chaobing, HU Junfeng. Multi-stage Noise Reduction Method with ESMK for Adaptive Feature Extraction of Rolling Bearings[J]. China Mechanical Engineering, 2021, 32(24): 2950-2959.

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http://www.cmemo.org.cn/CN/Y2021/V32/I24/2950

参考文献

［1］ZHAO X L，JIA M P. Fault Diagnosis of Rolling Bearing Based on Feature Reduction with Global-local Margin Fisher Analysis［J］. Neurocomputing, 2018, 315:447-464.
［2］RODRIGUEZ N. Combining Multi-scale Wavelet Entropy and Kernelized Classification for Bearing Multi-fault Diagnosis［J］. Entropy, 2019, 21(2):15-25.
［3］PEETERS C, GUILLAUME P. A Comparison of Cepstral Editing Methods as Signal Pre-processing Techniques for Vibration-based Bearing Fault Detection［J］. Mechanical Systems & Signal Processing, 2017, 9 (1):354-381.
［4］张龙,毛志德,杨世锡.基于包络谱带通峭度的改进谱峭度方法及在轴承诊断中的应用［J］.振动与冲击,2018,37(23):171-179.
ZHANG Long, MAO Zhide, YANG Shixi. An Improved Kurtogram Based on Band-pass Envelope Spectral Kurtosis with Its Application in Bearing Fault Diagnosis ［J］. Journal of Vibration and Shock, 2018, 37(23):171-179.
［5］苏文胜,王奉涛,张志新，等.EMD降噪和谱峭度法在滚动轴承早期故障诊断中的应用［J］.振动与冲击,2010,29(3):18-21.
SU Wensheng, WANG Fengtao, ZHANG Zhixin, et al. Application of EMD Denoising and Spectral Kurtosis in Early Fault Diagnosis of Rolling Element Bearings. ［J］. Journal of Vibration and Shock, 2010, 29(3):18-21.
［6］SELESNICK I W. Resonance-based Signal Decomposition:a New Sparsity-enabled Signal Analysis Method［J］. Signal Processing, 2011, 91(12):2793-2809.
［7］WANG H H, CHEN J, DONG G M， et al. Feature Extraction of Rolling Bearing's Early Weak Fault Based on EEMD and Tunable Q-factor Wavelet Transform［J］. Mechanical Systems and Signal Processing, 2014, 48(1/2):103-119.
［8］LI Y B, Liang X H, XU M Q, et al.Early Fault Feature Extraction of Rolling Bearing Based on ICD and Tunable Q-factor Wavelet Transform［J］. Mechanical Systems and Signal Processing, 2017, 8(6):204-223.
［9］WIGGINS R A. Minimum Entropy Deconvolution［J］. Geophysical Prospecting for Petrole, 1980, 16(1):21-35.
［10］MCDONALD G L, ZHAO Q, ZUO M J. Maximum Correlated Kurtosis Deconvolution and Application on Gear Tooth Chip Fault Detection［J］. Mechanical Systems & Signal Processing, 2012,33:237-255.
［11］MCDONALD G L, ZHAO Q. Multipoint Optimal Minimum Entropy Deconvolution and Convolution Fix:Application to Vibration Fault Detection［J］. Mechanical Systems and Signal Processing, 2017, 82:461-477.
［12］BARSZCZ T, SAWALHI N. Fault Detection Enhancement in Rolling Element Bearings Using the Minimum Entropy Deconvolution［J］. Archives of Acoustics, 2012. 37(2):131-141.
［13］RICCI R， BORGHESANI P, CHATTERTON S, et al. The Combination of Empirical Mode Decomposition and Minimum Entropy Deconvolution for Roller Bearing Diagnostics in Non-stationary Operation［C］∥ASME 2012—International Design Engineering Technical Conferences & Computers and Information in Engineering Conference IDETC/CIE. Chicago, 2012:47-63.
［14］SHANG Z W, GENG R, LI C， et al. Rolling Bearing Fault Diagnosis Method Based on MOMEDA and IEWT［J］. International Journal of Information and Management Sciences, 2018, 29(4):345-363.
［15］陈祥龙,冯辅周,张兵志,等.基于平方包络谱相关峭度的最优共振解调诊断滚动轴承故障［J］.机械工程学报,2018,54(21):90-100.
CHEN Xianglong, FENG Fuzhou, ZHANG Bingzhi, et al. Rolling Bearing Fault Diagnosis with Optimal Resonant Frequency Band Demodulation Based on Squared Envelope Spectral Correlated Kurtosis［J］. Journal of Mechanical Engineering, 2018, 54(21):90-100.
［16］SELESNICK I W. Wavelet Transform with Tunable Q-factor［J］. IEEE Transactions on Signal Processing, 2011, 59(8):3560-3575.
［17］STARCK J M, DONOHO D L. Image Decomposition via the Combination of Sparse Representations and a Variational Approach［J］. IEEE Transactions on Image Processing, 2005, 14(10):1570-1582.
［18］KENNEDY J. Particle Swarm Optimization［J］. Encyclopedia of Machine Learning, 2010,32(6):760-766.
［19］ENDO H, RANDALL R B. Enhancement of Autoregressive Model Based Gear Tooth Fault Detection Technique by The Use of Minimum Entropy Deconvolution Filter［J］. Mechanical Systems and Signal Processing, 2007, 21(2):906-919.

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滚动轴承自适应特征提取的包络谱多点峭度多级降噪方法

Multi-stage Noise Reduction Method with ESMK for Adaptive Feature Extraction of Rolling Bearings

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