摘要循环平稳分析是滚动轴承故障特征提取的重要方法之一，但在用于滚动特征提取时，存在因干扰成分较强而不能有效提取轴承故障特征的问题。为能在干扰环境中有效提取滚动轴承故障信息，基于循环谱分析提出一种鲁棒性滚动轴承故障特征提取方法。首先通过离散随机分离分析（discrete random separation，DRS）分离信号中的周期分量，提取其随机分量，随后用Teager能量算子（Teager energy operator，TEO）提取随机分量振动能量序列，再对该序列进行快速谱相关分析，采用基于能量熵的能量差异系数评价各循环频率（阶次）切片的能量强度，最终经熵加权降低无关干扰成分影响以有效提取故障特征。通过传统的快速谱峭度、快速谱相关和基于总变差去噪的快速谱相关分析方法与本方法对美国智能维护系统（intelligent maintenance systems，IMS）中心的滚动轴承振动数据以及实测齿轮箱复合故障实验信号进行对比分析，验证了本方法在滚动轴承故障诊断应用中的优势。
Abstract：Cyclic stationary analysis is one of the important methods for rolling bearing fault feature extraction. However, the bearing fault feature cannot be effectively extracted for the excessive irrelevant interference component. A robust rolling bearing fault feature extraction method based on cyclic spectrum analysis has been proposed to solve the problem in this paper. The random component of signal can be extracted by discrete random separation (DRS), and then the vibration energy sequence will be calculated by Teager energy operator (TEO) though the random component. With the fast spectral correlation analysis, the energy intensity of each cycle frequency (order) slice can be characterized by energy difference coefficient based on energy entropy. The influence of irrelevant interference component can be reduced by entropy weighting. Then, the fault feature of rolling bearing can be effectively extracted. It verified the advantage of this method in the application of rolling bearing fault diagnosis by experimental comparison of fast spectral kurtosis, fast spectral correlation and fast spectral correlation based on total variation de-noising.
晏云海，郭瑜，伍星. 基于循环谱分析的鲁棒性滚动轴承故障特征提取方法[J]. 振动与冲击, 2022, 41(6): 1-7.
YAN Yunhai，GUO Yu，WU Xing. Robust rolling bearing fault feature extraction method based on cyclic spectrum analysis. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(6): 1-7.
[1] Antoni J. Cyclostationarity by examples [J]. Mechanical Systems and Signal Processing, 2009, 23(4): 987-1036.
[2] Antoni J. Cyclic spectral analysis of rolling element bearing signals: Facts and fictions [J]. Journal of Sound and Vibration, 2007, 304(3): 497-529.
[3] Borghesani P, Antoni J. A faster algorithm for the calculation of the fast spectral correlation [J]. Mechanical Systems and Signal Processing, 2018, 111: 113-118.
[4] 唐贵基, 田甜, 庞彬. 基于总变差去噪和快速谱相关的滚动轴承故障诊断 [J]. 振动与冲击, 2019, 38(11): 187-193.
Tang Guiji, Tian Tian, Pang Bin. Rolling bearing fault diagnosis method based on total variation de-noising and fast spectral correlation [J]. Journal of Vibration and Shock, 2019, 38(11): 187-193.
[5] 万书亭, 彭勃. 基于非局部均值去噪和快速谱相关的滚动轴承早期故障诊断方法 [J]. 中南大学学报(自然科学版), 2020, 51(1): 76-85.
Wang Shuting, Peng Bo. Early fault diagnosis method of rolling bearing based on nonlocal mean denoising and fast spectral correlation [J]. Journal of Central South University (Science and Technology), 2020, 51(1): 76-85.
[6] Wu S L, Bechhoefer E, He D. A Practical Regime Prediction Approach for HUMS Applications [C]// American Helicopter Society 63rd Annual Forum, Riding the Wave of New Vertical Flight Technology Virginia Beach. VA. 2007: 1-8.
[7] Antoni J, Randall R B. Unsupervised noise cancellation for vibration signals: part II—a novel frequency-domain algorithm [J]. Mechanical Systems and Signal Processing, 2004, 18(1): 103-117.
[8] Kaiser J F. On a simple algorithm to calculate the 'energy' of a signal [C]// International Conference on Acoustics, Speech, and Signal Processing. [s.l.]: IEEE, 1990: 381-384.
[9] Yu Guo, Ting-Wei Liu, Jing Na, et al. Envelope order tracking for fault detection in rolling element bearings [J]. Journal of Sound and Vibration, 2012, 331:5644–5654.
[10] 祝小彦, 王永杰. 基于MOMEDA与Teager能量算子的滚动轴承故障诊断 [J]. 振动与冲击, 2018, 37(6): 104-110.
Zhu Xiaoyan, Wang Yongjie. Fault diagnosis of rolling bearings based on the MOMEDA and Teager energy operator [J]. Journal of Vibration and Shock, 2018, 37(6): 104-110.
[11] 陈海周, 王家序, 汤宝平, 等. 基于最小熵解卷积和Teager能量算子直升机滚动轴承复合故障诊断研究 [J]. 振动与冲击, 2017, 36(9): 45-50.
Chen Haizhou, Wang Jiaxu, Tang Baoping, et al. Helicopter rolling bearing hybrid faults diagnosis using minimum entropy deconvolution and Teager energy operator [J]. Journal of Vibration and Shock, 2017, 36(9): 45-50.
[12] 夏均忠, 苏涛, 张阳, 等. 基于EEMD能量熵及LS- SVM滚动轴承故障诊断 [J]. 噪声与振动控制, 2014, 34(3):170-175.
Xia Junzhong, Su Tao, Zhang Yang, et al. Fault Diagnosis Method of Rolling Bearings Based on Ensemble Empirical Mode Decomposition Energy Entropy and LS-SVM [J]. Noise and Vibration Control, 2014, 34(3):170-175.
[13] Xuejun Chen, Yongming Yang, Zhixin Cui, et al. Vibration fault diagnosis of wind turbines based on variational mode decomposition and energy entropy [J]. Energy, 2019, 174(1): 1100-1109.
[14] 黎敏, 阳建宏, 王晓景. 基于信息熵的循环谱分析方法及其在滚动轴承故障诊断中的应用 [J]. 振动工程学报, 2015, 28(1): 164-174.
Li Ming, Yang Jianhong, Wang Xiaojing. The cyclic spectrum density method based on entropy and its application to the fault diagnosis of rolling bearings [J]. Journal of Vibration Engineering, 2015, 28(1): 164-174.
[15] Qiu H, Lee J, Lin J. Wavelet Filter-based Weak Signature Detection Method and its Application on Roller Bearing Prognostics [J]. Journal of Sound and Vibration, 2006, 289(1): 1066-1090.
[16] https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data- repository/#bearing.
[17] Xiaoqiang Xu, Ming Zhao, Jing Lin, et al, Envelope harmonic -to-noise ratio for periodic impulses detection and its application to bearing diagnosis, Measurement. 2016, 91: 385 -397.
