摘要针对滚动轴承故障冲击信号周期性强且易被背景噪声淹没的特点，提出一种基于二阶全变分去噪（total variation denoising,TVD）与调制信号双谱（modulation signal bispectrum，MSB）的滚动轴承故障诊断方法。该方法首先利用二阶TVD处理故障信号，包络谱相关峭度被用于确定滤波过程中的参数λ；然后，为了进一步抑制噪声干扰，对滤波后信号进行MSB分析，选取故障特征频率占比最大的5个切片进行平均得到复合切片谱，提取出轴承故障特征；最后通过分析复合切片谱，判断轴承故障类型。将该方法运用到轴承故障仿真和实验信号，结果表明，本文提出的方法能够有效抑制强背景噪声的干扰，实现了滚动轴承的故障诊断，证明了方法的有效性。
Abstract：To solve the problem that the fault features of rolling element bearings are periodic but weak which usually are submerged in strong background noise, a fault diagnosis method based on the second order total variation denoising and modulation signal bispectrum was proposed.Firstly, TVD was applied to original vibration signal while the correlation kurtosis based on envelope spectrum was used as an index to select the optimal parameter λ.Then, the MSB was used to analyze the filtered signal to further suppress the interference of noise, the compound slice spectrum was composed by five selected slices based on index p.Finally, by analyzing the compound slice, the type of bearing fault was determined.The proposed method was applied in simulated and experimental fault signals of rolling element bearings.The results show that the method can reduce the effect of noise to realize accurate diagnosis of bearings’ faults.
朱丹宸,张永祥，赵磊，朱群伟. 基于TVD和MSB的滚动轴承故障特征提取[J]. 振动与冲击, 2019, 38(8): 103-109.
ZHU Danchen，ZHANG Yongxiang，ZHAO Lei，ZHU Qunwei. Fault feature extraction of rolling element bearings based on TVD and MSB. JOURNAL OF VIBRATION AND SHOCK, 2019, 38(8): 103-109.
[1] Yu J B. Bearing performance degradation assessment using locality preserving projections[J]. Expert Systems with Applications, 2011, 38(6):7440-7450.
[2] Rudin L I, Osher S, Fatemi E. Nonlinear total variation based noise removal algorithms[J]. Physica D Nonlinear Phenomena, 1992, 60(1–4):259-268.
[3] Figueiredo M A T, Dias J B, Oliveira J P, et al. On Total Variation Denoising: A New Majorization-Minimization Algorithm and an Experimental Comparisonwith Wavalet Denoising[C]. IEEE International Conference on Image Processing. IEEE, 2007:2633-2636.
[4] Zhang S, Wang Y, He Zhang S, Wang Y, He S, et al. Bearing fault diagnosis based on variational mode decomposition and total variation denoising[J]. Measurement Science & Technology, 2016, 27(7):075101.
[5] Yi C, Lv Y, Dang Z, et al. A Novel Mechanical Fault Diagnosis Scheme Based on the Convex 1-D Second-Order Total Variation Denoising Algorithm[J]. Applied Sciences, 2016:403.
[6] Alwodai A, Wang T, Chen Z, et al. A Study of Motor Bearing Fault Diagnosis using Modulation Signal Bispectrum Analysis of Motor Current Signals[J]. Journal of Signal & Information Processing, 2013, 04(3):72-79.
[7] Zhang R, Gu F, Mansaf H, et al. Gear wear monitoring by modulation signal bispectrum based on motor current signal analysis[J]. Mechanical Systems & Signal Processing, 2017, 94:202-213.
[8] Rehab I, Tian X, Gu F, et al. The fault detection and severity diagnosis of rolling element bearings using modulation signal bispectrum[J]. 2014.
[9] Tian X, Gu J X, Rehab I, et al. A robust detector for rolling element bearing condition monitoring based on the modulation signal bispectrum and its performance evaluation against the Kurtogram[J]. Mechanical Systems & Signal Processing, 2018, 100:167-187.
[10] 刘尚坤, 唐贵基, 何玉灵. Teager能量算子结合MCKD的滚动轴承早期故障识别[J]. 振动与冲击, 2016, 35(15):98-102.
LIU Shang-kun, TANG Gui-ji, HE Yu-ling. Incipient fault diagnosis of rolling bearing based on Teager energy operator and maximum correlated kurtosis deconvolution. JOURNAL OF VIBRATION AND SHOCK, 2016, 35(15): 98-102.
[11] Antoni J. Fast computation of the kurtogram for the detection of transient faults[J]. Mechanical Systems & Signal Processing, 2007, 21(1):108-124.
