基于奇异值分解和独立分量分析的滚动轴承故障诊断方法

doi:10.3969/j.issn.1000-1158.2022.06.11

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摘要针对强背景噪声下难以提取滚动轴承早期故障信号中故障特征频率的问题,提出奇异值分解和独立分量分析相结合的滚动轴承故障诊断方法。该方法首先利用相空间重构将一维时域矩阵拓展到高维矩阵,得到吸引子轨迹矩阵;然后对轨迹矩阵进行奇异值分解降噪,依据奇异值差分谱阈值原则选取相应阶次分量进行重组构造虚拟噪声通道;接着将重组信号和观测信号进行独立分量分析分离;最后利用能量算子解调方法提取出有效的故障特征分量,进而识别故障类型。滚动轴承故障诊断实验和仿真结果表明该方法有效可行。

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	陈剑
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	刘幸福
	蔡坤奇

关键词 ：计量学, 滚动轴承, 故障诊断, 奇异值分解, 独立分量分析, 降噪

Abstract：To solve the problem that it is difficult to extract the characteristic frequency of the fault in the early fault signal of the rolling bearing under strong background noise, the signal analysis method of singular value decomposition-independent component analysis was proposed. At first, phase space reconstruction was used to extend the one-dimensional time-domain signal to higher dimensions, and obtain the attractor trajectory matrix. Then singular value decomposition was performed on the trajectory matrix to reduce noise. According to the singular value difference spectrum threshold principle, the corresponding order components were selected for recombination to construct the virtual noise channel. Then the recombined signal and the observation signal were separated by ICA. Finally the energy operator demodulation method was used to extract the effective fault feature components to identify the fault type. The fault diagnosis experiment and simulation results of rolling bearing showed that the method is effective and feasible.

Key words： metrology rolling bearings fault diagnosis singular value decomposition independent component analysis noise reduction

收稿日期: 2021-01-26 发布日期: 2022-06-30

PACS:

TB936

基金资助:国家自然科学基金青年基金(11604070);安徽省重大科技项目(17030901049)

通讯作者: 刘圆圆(1996-),男,安徽合肥人,合肥工业大学研究生。主要研究方向为信号处理、旋转机械故障诊断。Email: 2434730930@qq.com E-mail: hfgd8216@126.com

作者简介: 陈剑(1962-),男,河南固始人,合肥工业大学教授,主要从事汽车NVH控制技术、机械系统故障特征提取与诊断的研究。Email: hfgd8216@126.com

引用本文:

陈剑,刘圆圆,黄凯旋,杨斌,刘幸福,蔡坤奇. 基于奇异值分解和独立分量分析的滚动轴承故障诊断方法[J]. 计量学报, 2022, 43(6): 776-784.
CHEN Jian,LIU Yuan-yuan,HUANG Kai-xuan,YANG Bin,LIU Xing-fu,CAI Kun-qi. Rolling Bearing Fault Diagnosis Method Based on Singular Value Decomposition and Independent Component Analysis. Acta Metrologica Sinica, 2022, 43(6): 776-784.

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http://jlxb.china-csm.org:81/Jwk_jlxb/CN/10.3969/j.issn.1000-1158.2022.06.11 或 http://jlxb.china-csm.org:81/Jwk_jlxb/CN/Y2022/V43/I6/776

［1］Donoho D L, Johnstone I M. Adapting to Unknown Smoothness Via Wavelet Shrickage ［J］. Journal of the America Statistical Association, 1995, 90(432): 1200-1224.
［2］陈剑, 庄学凯, 吕伍佯, 等. 基于IVMD和马田系统的滚动轴承故障检测方法［J］. 计量学报, 2019, 40(6): 1083-1087.
Chen J, Zhuang X K, Lv W Y, et al. Fault Diagnosis of Rolling Bearing Using Mahalanobis-Taguchi System Based on IVMD ［J］. Acta metrologica Sinica, 2019, 40(6): 1083-1087.
［3］刑婷婷, 关阳, 刘子涵, 等. 基于变分模态分解和奇异值分解的频率相近信号分离方法［J］. 计量学报, 2020, 41(11): 1404-1409.
Xing T T, Guan Y, Liu Z H, et al. Similar Frequency Signal Separation Based on VMD and Singular Vaule Decomposition ［J］. Acta metrologica Sinica, 2020, 41(11): 1404-1409.
［4］Chen Y M, Zi Y Y, Cao H R, et al. A data-driven threshold for wavelet sliding window denoising in mechanical fault detection ［J］. Science China(Technological Sciences), 2014, 57(3): 589-597.
［5］Huang N E, Shen Z, Long S R. The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis ［J］. Proc R Soc, 1998, 454(1971): 903-995.
［6］钱征文, 程礼, 李应红. 利用奇异值分解的信号降噪方法［J］. 振动、测试与诊断, 2011, 31(4): 459-463.
Qian Z W, Cheng L, Li Y H. Signal denoising method by means of SVD ［J］. Journal of Vibration Measurement & Diagnosis, 2011, 31(4): 459-463.
［7］Hyvarinen A. Fast and robust fixed-point algorithms for independent component analysis ［J］. IEEE Trans. Neural Networks, 1999, 10(3): 626-634.
［8］张俊红, 李林洁, 马文朋, 等. EMD-ICA联合降噪在滚动轴承故障诊断中的应用［J］. 中国机械工程, 2013, 24(11): 1468-1472.
Zhang J H, Li L J, Ma W P, et al. Application of EMD-ICA to Fault Diagnosis of Rolling Bearings ［J］. China Mechanical Engineering, 2013, 24(11): 1468-1472.
［9］吕跃刚, 何洋洋. EWT和ICA联合降噪在轴承故障诊断中的应用［J］. 振动与冲击, 2019, 38(16): 42-48+70.
Lv Y G, He Y Y. Application of an EWT-ICA combined method in fault diagnosis of rolling bearings ［J］. Journal of Vibration and Shock, 2019, 38(16): 42-48, 70.
［10］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 J L, Zhu C M, Jiang Z L, et al. Application of LMD-ICA to Fault Diagnosis of Rolling Bearings ［J］. China Mechanical Engineering, 2016, 27(7): 904-910.
［11］马增强, 柳晓云, 张俊甲, 等. VMD和ICA联合降噪方法在轴承故障诊断中的应用［J］. 振动与冲击, 2017, 36(13): 201-207.
Ma Z Q, Liu X Y, Zhang J J, et al. Application of VMD-ICA combined method in fault diagnosis of rolling bearings ［J］. Journal of Vibration and Shock, 2017, 36(13): 201-207.
［12］郑慧峰, 喻桑桑, 王月兵, 等. 基于经验模态分解和奇异值分解的振动声调制信号分析方法研究［J］. 计量学报, 2016, 37(4): 398-401.
Zheng H H, Yu S S, Wang Y B, et al. Research on the Analysis Method of Vibro-acoustic Modulation Signal Based on EMD and SVD ［J］. Acta metrologica Sinica, 2016, 37(4): 398-401.
［13］孟宗, 吕蒙, 殷娜, 等. 基于改进变分模态分解的滚动轴承故障诊断方法［J］. 计量学报, 2020, 41(6): 717-723.
Meng Z, Lv M, Yin N, et al. Fault Diagnosis Method of Rolling Bearing Based on Improved Variational Mode Decomposition ［J］. Acta metrologica Sinica, 2020, 41(6): 717-723.
［14］陈剑, 陶善勇, 王维, 等. 基于周期势函数的自适应二阶欠阻尼随机共振信号增强方法［J］. 计量学报, 2019, 40(4): 681-685.
Chen J, Tao S Y, Wang W, et al. Adaptive Second-order Underdamped Stochastic Resonance Signal Enhancement Method Based on Periodic Potential Function ［J］. Acta metrologica Sinica, 2019, 40(4): 681-685.
［15］范金峰. 脑电非线性时间序列仿真研究［D］. 合肥: 中国科学技术大学, 2007.
［16］赵洪山, 郭双伟, 高夺. 基于奇异值分解和变分模态分解的轴承故障特征提取［J］. 振动与冲击, 2016, 35(22): 183-188.
Zhao H S, Guo S W, Gao D. Feature Extraction Method of Rolling Bearing Fault Based on Singular Value Decomposition-morphology Filter and Empirical Mode Decomposition ［J］. Journal of Vibration and Shock, 2016, 35 (22): 183-188.