Rolling Bearing Fault Diagnosis of Maximum Correlation Kurtosis Deconvolution Combining with Fourier Decomposition Method
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摘要: 针对强背景噪声环境下滚动轴承故障特征难以提取的问题，提出一种基于最大相关峭度反褶积（MCKD）与傅里叶分解方法（FDM）相结合的滚动轴承故障诊断方法。首先采用MCKD对振动信号去噪、提取与故障相关的冲击成分；其次，采用FDM对去噪信号进行分解，得到若干个瞬时频率具有物理意义的傅里叶固有频带函数和一个残余分量之和；第三，依据各个模态与去噪信号的相关性提取包含故障信息的最优模态分量，并对它们进行重构；最后，计算重构信号的包络谱，从谱图中读取故障信息。将所提故障诊断方法应用于滚动轴承故障仿真和实验数据分析，并通过与现有方法进行对比，结果表明，该方法优于所对比的方法。Abstract: Aiming at the problem that it is difficult to extract fault features of rolling bearing form strong background noise, a fault diagnosis method based on maximum correlation kurtosis deconvolution (MCKD) and Fourier decomposition method (FDM) is proposed. First, MCKD is used to denoise the vibration signal and extract the impact component related with failure. Second, the denoising signal is decomposed by FDM, and several Fourier intrinsic band functions (FIBFs) with physical significance and one residual component are obtained. Third, the correlation between each FIBF and denoising signal is computed to select the optimal components that contain main fault information for reconstruction. Finally, the envelope spectrum of the reconstructed signal is calculated and the fault information is read from envelope spectrum for diagnostics. The proposed fault diagnosis method is applied to simulation and experimental data analysis of faulty rolling bearing by comparing with the existing methods. The analysis results show that the proposed method is superior to the existing methods of comparison.
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表 1 FIBF的相关系数
分量 y1 y2 y3 y4 y5 y6 y7 相关系数 0.0001 0.0899 0.0907 0.1438 0.2302 0.2271 0.2199 分量 y8 y9 y10 y11 y12 y13 y14 相关系数 0.2827 0.3213 0.2926 0.3362 0.3996 0.2372 0.2455 分量 y15 y16 y17 y18 y19 y20 r 相关系数 0.1674 0.1795 0.1592 0.1801 0.1751 0.0586 0 
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表 2 FIBF与去噪信号的相关系数
分量 y1 y2 y3 y4 y5 y6 y7 相关系数 0.0007 0.0738 0.1642 0.0946 0.1329 0.1313 0.1131 分量 y8 y9 y10 y11 y12 y13 y14 相关系数 0.1278 0.1354 0.1179 0.1766 0.2166 0.2061 0.2884 分量 y15 y16 y17 y18 y19 y20 y21 相关系数 0.2664 0.3246 0.2479 0.2554 0.2623 0.2147 0.2071 分量 y22 y23 y24 y25 y26 y27 y28 相关系数 0.1701 0.2032 0.1482 0.1208 0.1099 0.1687 0.1023 分量 y29 y30 y31 y32 y33 y34 r 相关系数 0.1011 0.0879 0.0925 0.0745 0.0547 0.0705 0
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[1] Brown D N. Envelope analysis detects bearing faults before major damage occurs[J]. Pulp and Paper, 1989, 63(13): 113-117 [2] 郑近德, 程军圣, 杨宇. 一种新的估计瞬时频率的方法-经验包络法[J]. 振动与冲击, 2012, 31(17): 86-90 doi: 10.3969/j.issn.1000-3835.2012.17.014Zheng J D, Cheng J S, Yang Y. A new instantaneous frequency estimation approach-empirical envelope method[J]. Journal of Vibration and Shock, 2012, 31(17): 86-90 (in Chinese) doi: 10.3969/j.issn.1000-3835.2012.17.014 [3] Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 1998, 454(1971): 903-995 doi: 10.1098/rspa.1998.0193 [4] 于德介, 程军圣, 杨宇. 机械故障诊断的Hilbert-Huang变换方法[M]. 北京: 科学出版社, 2007Yu D J, Cheng J S, Yang Y. Hilbert-Huang transform method for mechanical fault diagnosis[M]. Beijing: Science Press, 2007 (in Chinese) . [5] Hou T Y, Yan M P, Wu Z H. A variant of the EMD method for multi-scale data[J]. Advances in Adaptive Data Analysis, 2009, 1(4): 483-516 doi: 10.1142/S179353690900031X [6] Xu Y S, Zhang H Z. Recent mathematical developments on empirical mode decomposition[J]. Advances in Adaptive Data Analysis, 2009, 1(4): 681-702 doi: 10.1142/S1793536909000242 [7] 高强, 杜小山, 范虹, 等. 滚动轴承故障的EMD诊断方法研究[J]. 振动工程学报, 2007, 20(1): 15-18 doi: 10.3969/j.issn.1004-4523.2007.01.003Gao Q, Du X S, Fan H, et al. An empirical mode decomposition based method for rolling bearing fault diagnosis[J]. Journal of Vibration Engineering, 2007, 20(1): 15-18 (in Chinese) doi: 10.3969/j.issn.1004-4523.2007.01.003 [8] 蔡艳平, 李艾华, 石林锁, 等. 基于EMD与谱峭度的滚动轴承故障检测改进包络谱分析[J]. 振动与冲击, 2011, 30(2): 167-172, 191 doi: 10.3969/j.issn.1000-3835.2011.02.033Cai Y P, Li A H, Shi L S, et al. Roller bearing fault detection using improved envelope spectrum analysis based on EMD and spectrum kurtosis[J]. Journal of Vibration and Shock, 2011, 30(2): 167-172, 191 (in Chinese) doi: 10.3969/j.issn.1000-3835.2011.02.033 [9] 李华, 伍星, 刘韬, 等. 基于信息熵优化变分模态分解的滚动轴承故障特征提取[J]. 振动与冲击, 2018, 37(23): 219-225Li H, Wu X, Liu T, et al. Bearing fault feature extraction based on VMD optimized with information entropy[J]. Journal of Vibration and Shock, 2018, 37(23): 219-225 (in Chinese) [10] Dragomiretskiy K, Zosso D. Variational mode decomposition[J]. IEEE Transactions on Signal Processing, 2014, 62(3): 531-544 doi: 10.1109/TSP.2013.2288675 [11] 姜战伟, 郑近德, 潘海洋, 等. POVMD与包络阶次谱的变工况滚动轴承故障诊断[J]. 振动、测试与诊断, 2017, 37(3): 609-616Jiang Z W, Zheng J D, Pan H Y, et al. Fault diagnosis under variable conditions based on parameter optimized variational mode decomposition and envelope order spectrum[J]. Journal of Vibration, Measurement & Diagnosis, 2017, 37(3): 609-616 (in Chinese) [12] Singh P, Joshi S D, Patney R K, et al. The Fourier decomposition method for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2017, 473(2199): 20160871 doi: 10.1098/rspa.2016.0871 [13] 张国瑞, 王旭元, 郭文斌. 基于傅里叶分解与1.5维Teager能量谱的滚动轴承故障诊断方法[J]. 机械传动, 2017, 41(3): 191-196Zhang G R, Wang X Y, Guo W B. Fault diagnosis method of rolling bearing based on Fourier decomposition method and 1.5-dimensional Teager energy spectrum[J]. Journal of Mechanical Transmission, 2017, 41(3): 191-196 (in Chinese) [14] 张瑞. 旋转机械碰摩声发射故障诊断研究[D]. 南京: 东南大学, 2016Zhang R. Research on fault diagnosis in rotary machinery acoustic emission signal[D]. Nanjing: Nanjing Southeast University, 2016 (in Chinese) [15] Wiggins R A. Minimum entropy deconvolution[J]. Geoexploration, 1978, 16(1-2): 21-35 doi: 10.1016/0016-7142(78)90005-4 [16] Li G, Zhao Q. Minimum entropy deconvolution optimized sinusoidal synthesis and its application to vibration based fault detection[J]. Journal of Sound and Vibration, 2017, 390: 218-231 doi: 10.1016/j.jsv.2016.11.033 [17] 王宏超, 陈进, 董广明. 基于最小熵解卷积与稀疏分解的滚动轴承微弱故障特征提取[J]. 机械工程学报, 2013, 49(1): 88-94 doi: 10.3901/JME.2013.01.088Wang H C, Chen J, Dong G M. Fault diagnosis method for rolling bearing's weak fault based on minimum entropy deconvolution and sparse decomposition[J]. Journal of Mechanical Engineering, 2013, 49(1): 88-94 (in Chinese) doi: 10.3901/JME.2013.01.088 [18] McDonald G L, Zhao Q, Zuo M J. Maximum correlated Kurtosis deconvolution and application on gear tooth chip fault detection[J]. Mechanical Systems and Signal Processing, 2012, 33: 237-255 doi: 10.1016/j.ymssp.2012.06.010 [19] 唐贵基, 王晓龙. 最大相关峭度解卷积结合1.5维谱的滚动轴承早期故障特征提取方法[J]. 振动与冲击, 2015, 34(12): 79-84Tang G J, Wang X L. Feature extraction for rolling bearing incipient fault based on maximum correlated kurtosis deconvolution and 1.5 dimension spectrum[J]. Journal of Vibration and Shock, 2015, 34(12): 79-84 (in Chinese) [20] 周福昌. 基于循环平稳信号处理的滚动轴承故障诊断方法研究[D]. 上海: 上海交通大学, 2006Zhou F C. Research on the fault diagnosis method of rolling element bearing based on cyclostationary signal processing[D]. Shanghai: Shanghai Jiaotong University, 2006 (in Chinese) [21] 刘文明. 强背景噪声下列车轴承振动信号故障特征分离方法研究[D]. 石家庄: 石家庄铁道大学, 2018Liu W M. Research on the fault characteristic separation method of train bearings vibration signal under strong background noise[D]. Shijiazhuang: Shijiazhuang Tiedao University, 2018 (in Chinese) [22] 陈果. 滚动轴承早期故障的特征提取与智能诊断[J]. 航空学报, 2009, 30(2): 362-367 doi: 10.3321/j.issn:1000-6893.2009.02.028Chen G. Feature Extraction and intelligent diagnosis for ball bearing early faults[J]. Acta Aeronautica et Astronautica Sinica, 2009, 30(2): 362-367 (in Chinese) doi: 10.3321/j.issn:1000-6893.2009.02.028 [23] Miao Y H, Zhao M, Lin J, et al. Application of an improved maximum correlated kurtosis deconvolution method for fault diagnosis of rolling element bearings[J]. Mechanical Systems and Signal Processing, 2017, 92: 173-195 doi: 10.1016/j.ymssp.2017.01.033 -
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