Incipient rolling element bearing weak fault feature extraction based on adaptive second-order stochastic resonance incorporated by mode decomposition
Incipient rolling element bearing weak fault feature extraction based on adaptive second-order stochastic resonance incorporated by mode decomposition
复制标题
基于模态分解的自适应二阶随机共振的早期滚动轴承弱故障特征提取
DOI：
10.1016/j.measurement.2019.05.052
复制
发表时间：
2019-10
期刊：
影响因子：
5.6
通讯作者：
Li Hongkun
中科院分区：
文献类型：
--
作者：
He Changbo;Niu Pei;Yang Rui;Wang Chaoge;Li Zhixiong;Li Hongkun
由于轴承早期故障特征极其微弱，且受强噪声干扰，给早期故障预警工作带来很大困难。针对传统特征提取方法不能有效识别故障频率的问题，提出了一种基于自适应噪声的完全集成经验模态分解（CEEMDAN）和改进的自适应欠阻尼随机共振（AUSR）相结合的故障频率识别方法。首先简要介绍了经典模式分解方法EMD、EEMD和CEEMD的原理和不足。针对这些缺点，采用CEEMDAN对目标信号进行分解，提取敏感IMF。在此基础上，考虑阻尼因素，对超声波共振进行了更一般的理论分析。在此基础上，提出了一种基于遗传算法的AUSR方法。通过不同的仿真实例分析，验证了CEEMDAN方法相对于其他模态分解方法的优越性和所提出的整体分析方案的有效性。随后，将该方法进一步应用于两种情况下的轴承微弱故障特征频率的增强和提取实验信号。分析结果表明，该方法能显著提高系统的特征频率，进一步证明了该方法在工程应用中的有效性和优越性。
Incipient bearing fault characteristic is extremely weak and interfered by strong noise, which makes the early fault warning work very difficult. Considering traditional characteristic extraction methods cannot identify the fault frequency effectively, a method is proposed in this paper based on the cooperation of complete ensemble EMD with adaptive noise (CEEMDAN) and improved adaptive underdamped stochastic resonance (AUSR). Specifically, the principles and shortcomings of classical mode decomposition methods EMD, EEMD and CEEMD are briefly introduced first. Aiming at these shortcomings, CEEMDAN is adopted to decompose target signal for the extraction of sensitive IMF. Then, a more general theoretical analysis of USR is conducted by taking damping factor into account. Furthermore, an AUSR method is proposed based on GA. Both the superiority of CEEMDAN compared with other mode decomposition methods and the effectiveness of proposed overall analysis scheme are demonstrated by different cases of simulation analysis. Subsequently, the proposed method is further applied on two cases of experimental signals for bearing weak fault characteristic frequency enhancement and extraction. The analyzed results show that the characteristic frequency can be significantly enhanced with the help of proposed method, which further demonstrates its effectiveness and superiority in engineering application.
登录
查看更多内容
欠阻尼阶跃二阶随机共振对弱信号检测的影响
影响因子：
2.9
作者：
Lu, Siliang;He, Qingbo;Kong, Fanrang
通讯作者：
Kong, Fanrang
影响因子：
8.4
作者：
Glowacz, Adam
通讯作者：
Glowacz, Adam
时频压缩与广义解调相结合的变速轴承故障诊断
DOI：
10.1109/tim.2018.2868519
发表时间：
2019-08-01
影响因子：
5.6
作者：
Huang, Weiguo;Gao, Guanqi;Zhu, Zhongkui
通讯作者：
Zhu, Zhongkui
基于差分方程的经验模态分解及其在多故障振动信号分离增强中的应用
DOI：
10.1080/10236198.2016.1254206
发表时间：
2017-01-01
影响因子：
1.1
作者：
Li, Zhixiong;Jiang, Yu;Peng, Zhongxiao
通讯作者：
Peng, Zhongxiao
影响因子：
3.9
作者：
Wang, Zhijian;Zhou, Jie;He, Gaofeng
通讯作者：
He, Gaofeng