非线性调频模态分解-同步提取变换方法及其在滚动轴承故障诊断中的应用

doi:10.3969/j.issn.1000-1093.2021.06.023

摘要/Abstract

摘要： 传统同步提取变换（SET）方法在处理多分量非平稳复杂信号时，各相邻分量的瞬时频率差要大于窗函数频率支撑范围的2倍，否则时频结果易发生频率混叠，而工程信号常常难以满足。此外，在处理高噪的复杂信号时，其时频分辨率往往不理想。针对此不足，将非线性调频模态分解(VNCMD)引入SET中，提出一种VNCMD-SET的故障诊断方法。利用VNCMD通过结合解调手段以及变分模态分解的联合优化方案来有效处理频率临近甚至交叉的非平稳信号，对故障信号进行分解重构，对该重构信号进行SET处理。将所提方法与传统SET方法进行对比研究，并进行实验验证。仿真和实验结果表明：VNCMD-SET方法明显优于传统SET方法，克服了传统SET方法的不足；VNCMD-SET方法能有效提取出故障信号的频率特征，且不发生混叠，同时具有一定的抗噪性能。

关键词: 滚动轴承, 非线性调频模态分解, 同步提取变换, 故障诊断

Abstract: When the traditional synchroextracting transform (SET) method is used to process the multi-component non-stationary signals, the instantaneous frequency difference of adjacent components is greater than 2 times of the frequency support range of the window function. Otherwise the time-frequency result is prone to frequency aliasing. However, the actual signal often can not satisfy this condition. In additional, when traditional SET method is used to process complex signal with high noise, its time-frequency resolution is often not ideal. Based on this deficiency, the variational nonlinear chirp mode decomposition (VNCMD) is introduced into SET in this paper, and a fault diagnosis method of VNCMD-SET is proposed. In the proposed method, VNCMD is used to process non-stationary signals with close or even cross frequencies by combining demodulation and variational modal decomposition. The fault signal is decomposed and reconstructed by VNCMD, then the reconstructed signal is processed by SET. The proposed method was compared with the traditional SET method, and was verified through experiment. The simulated and experimental results show that the proposed VNCMD-SET method is superior to the traditional SET method and overcomes the deficiency in the traditional SET method; the proposed method can effectively extract the frequency characteristics of fault signal, suppresses the aliasing, and has a certain anti-noise performance.

Key words: faultdiagnosis, nonlinearfrequencymodulationmodedecomposition, synchroextractingtransform, rollingbearing

中图分类号:

TH133.33⁺4

李志农, 胡志峰, 毛清华, 张旭辉, 陶俊勇. 非线性调频模态分解-同步提取变换方法及其在滚动轴承故障诊断中的应用[J]. 兵工学报, 2021, 42(6): 1324-1330.

LI Zhinong, HU Zhifeng, MAO Qinghua, ZHANG Xuhui, TAO Junyong. Variational Nonlinear Chirp Mode Decomposition-synchroextracting Transform Method and Its Application in Fault Diagnosisof Rolling Bearing[J]. Acta Armamentarii, 2021, 42(6): 1324-1330.

参考文献

［1］刘岩, 伍星, 刘韬, 等. 基于自适应MOMEDA与VMD的滚动轴承早期故障特征提取[J]. 振动与冲击, 2019, 38(23):219-229.
LIU Y, WU X, LIU T, et al. Feature extraction for rolling bearing incipient faults based on adaptive MOMEDA and VMD [J]. Vibration and Shock, 2019, 38(23):219-229.（in Chinese）
[2］李恒, 张氢, 秦仙蓉, 等. 基于短时傅里叶变换和卷积神经网络的轴承故障诊断方法[J].振动与冲击, 2018, 37(19):124-131.
LI H, ZHANG Q, QIN X R, et al. Fault diagnosis method for rolling bearings based on short-time Fourier transform and convolution neural network[J]. Vibration and Shock,2018, 37(19):124-131. (in Chinese)
[3］沈金伟, 石林锁. 滚动轴承故障诊断的改进小波变换谱峭度法[J]. 轴承, 2010(8):46-49.
SHEN J W, SHI L S. Improved spectral Kurtosis algorithm based on wavelet transformation for rolling bearing fault diagnosis [J]. Bearing, 2010(8):46-49.(in Chinese)
[4］赵培洪, 平殿发, 邓兵, 等. 魏格纳-维尔分布交叉项抑制方法综述[J]. 探测与控制学报, 2010, 32(1):23-29.
ZHAO P H, PING D F, DENG B, et al. Review of cross-items suppression methods of Wegener-Vill distribution[J].Journal of Detection & Control, 2010, 32(1):23-29.(in Chinese)
[5］ YU G, YU M J, XU C Y. Synchroextracting transform[J]. IEEE Transactions on Industrial Electronics, 2017, 64(10): 8042-8054.
[6］ INGRID D, LU J F, WU H T. Synchrosqueezed wavelet transforms: an empirical mode decomposition-like tool[J]. Applied and Computational Harmonic Analysis, 2011, 30(2): 243-261.
[7］ CHEN H, KANG J X, CHEN Y C, et al. An improved time-frequency analysis method for hydrocarbon detection based on EWT and SET[J]. Energies，2017, 10(8): 1090-1102.
[8］ YU G. Local maximum synchrosqueezing transform: an energy-concentrated time-frequency analysis tool[J]. Mechanical Systems and Signal Processing, 2019, 117: 537-552.
[9］赵妍, 崔浩瀚, 荣子超. 次同步振荡在线监测的同步提取变换和朴素贝叶斯方法[J]. 电力系统自动化, 2019, 43(3):187-193.
ZHAO Y, CUI H H, RONG Z C. On-line monitoring of subsynchronous oscillation based on synchrextracting transform and Naive Bayes method [J]. Automation of Electric Power Systems, 2019, 43(3):187-193.(in Chinese)
[10］刘诣, 谢雄杰, 罗晓庆, 等. 计及信号传输特性的变压器套管末屏高频局部放电信号时频分析方法[J].高电压技术, 2020, 46(2):594-602.
LIU Y, XIE X J, LUO X Q, et al. Time-frequency analysis method for HF partial discharge signal from power transformer bushing tap with signal propagation characteristics[J]. High Voltage Engineering, 2020, 46(2):594-602.(in Chinese)
[11］张琳. 基于同步提取变换的变转速工况下滚动轴承故障诊断方法研究[D]. 石家庄：石家庄铁道大学, 2019.
ZHANG L. Research on fault diagnosis methodof rolling bearing based on synchronous extraction transform[D]. Shijiazhuang： Shijiazhuang Tiedao University, 2019.(in Chinese)
[12］ MA Z Q, RUAN W Y, CHEN M Y, et al. An improved time-frequency analysis method for instantaneous frequency estimation of rolling bearing[J]. Shock and Vibration, 2018(3):1-18.
[13］ LIU H, XIANG J W. Kernel regression residual decomposition-based synchroextracting transform to detect faults in mechanical systems[J]. ISA Transactions, 2019, 87: 251-263.
[14］ ZHANG L, LIU Y Q. Rolling bearing fault diagnosis based on synchroextracting transform under variable rotational speed conditions[C]∥Proceedings of 2018 Prognostics and System Health Management Conference. Chongqing, China: IEEE, 2018: 1285-1290.
[15］孙猛猛, 郅伦海. 环境风激励下中信广场的模态参数识别[J]. 振动, 测试与诊断, 2020, 40(2):264-271,417.
SUN M M, ZHI L H. Modal parameter identification of CITIC Plaza under ambient wind excitation[J]. Journal of Vibration, Measurement and Diagnosis, 2020, 40(2):264-271,417.(in Chinese)
[16］康佳星. 同步提取变换算法的改进研究及其在地震信号分析中的应用[D]. 成都：成都理工大学, 2018.
KANG J X. Improvement of synchroextracting transform algorithm and its application in seismic signal analysis[D]. Chengdu: Chengdu University of Technology, 2018.(in Chinese)
[17］周俊, 马建林, 徐华, 等. EMD降噪在高速铁路路基沉降预测中的应用[J]. 振动与冲击, 2016, 35(8):66-72.
ZHOU J, MA J L, XU H, et al. Application of EMD denoising method in subgrade settlement prediction of high-speed railways[J]. Journal of Vibration and Shock, 2016, 35(8): 66-72.(in Chinese)
[18］ GILLES J. Empirical wavelet transform[J]. IEEE Transactions on Signal Processing, 2013, 61(16): 3999-4010．
[19］ CHEN S Q, DONG X J, PENG Z K, et al. Nonlinear chirp mode decomposition: a variational method[J]. IEEE Transactions on Signal Processing, 2017, 65(22): 6024-6037.
[20］ DRAGOMIRETSKIY K, ZOSSO D. Variational mode decomposition[J]. IEEE Transactions on Signal Processing, 2014, 62(3): 531-544.

[1]	李良钰，苏铁熊，马富康，吴小军，徐春龙. 基于集合经验模态分解-支持向量机的高压共轨系统故障诊断方法[J]. 兵工学报, 2022, 43(5): 992-1001.
[2]	朱敏，许爱强，许晴，李睿峰. 基于改进多层核超限学习机的模拟电路故障诊断[J]. 兵工学报, 2021, 42(2): 356-369.
[3]	李佳蔚，崔涛，刘宇航，师帅楠，孙强. 基于排气温度动态模型的在线观测器研究[J]. 兵工学报, 2019, 40(8): 1562-1571.
[4]	郭伟超，赵怀山，李成，李言，汤奥斐. 基于小波包能量谱与主成分分析的轴承故障特征增强诊断方法[J]. 兵工学报, 2019, 40(11): 2370-2377.
[5]	翟禹尧，史贤俊，吕佳朋. 基于广义随机Petri网的导弹系统测试性建模与指标评估方法研究[J]. 兵工学报, 2019, 40(10): 2070-2079.
[6]	张伟，刘星，许爱强，平殿发. l_p范数约束的模拟电路3层多核故障诊断模型[J]. 兵工学报, 2018, 39(7): 1352-1363.
[7]	黄大荣, 陈长沙，柯兰艳，赵玲，米波，孙国玺. 误差影响下滚动轴承多重故障模态特征信号的盲源分离方法[J]. 兵工学报, 2018, 39(7): 1419-1428.
[8]	任海锋，潘宏侠. 基于多分形特征的枪械自动机裂纹故障诊断[J]. 兵工学报, 2018, 39(3): 457-462.
[9]	王英敏，崔涛，张付军，董天普. 基于局部线性模型树模型的柴油机进气系统漏气和堵塞故障诊断[J]. 兵工学报, 2017, 38(8): 1457-1468.
[10]	黄大荣, 陈长沙，孙国玺，赵玲，米波. 复杂装备轴承多重故障的线性判别分析与反向传播神经网络协作诊断方法[J]. 兵工学报, 2017, 38(8): 1649-1657.
[11]	李志农，朱明. 基于变分模态分解的机械故障诊断方法研究[J]. 兵工学报, 2017, 38(3): 593-599.
[12]	李海广，潘宏侠，任海锋. 基于冲击响应谱特征提取的自动机裂纹故障诊断[J]. 兵工学报, 2016, 37(9): 1744-1752.
[13]	田恒，段富海，江秀红，桑勇. 基于准信息熵的测试性D矩阵故障诊断新算法[J]. 兵工学报, 2016, 37(5): 923-928.
[14]	李志农，王海峰，肖尧先. 基于分数阶微积分的裂纹转子系统非线性动力学特性研究[J]. 兵工学报, 2015, 36(9): 1790-1798.
[15]	王福元，赵建社，唐霖. 多系统集成电解加工控制策略与故障诊断方法研究[J]. 兵工学报, 2015, 36(6): 1074-1081.