摘要
滚动轴承的故障信息一般包含在振动信号中,如何有效地从原始信号中挖掘出故障信息,是机械设备健康状况检测和评估的关键.为此,本文提出了层次逆向散布熵特征提取方法,以提高故障诊断的准确性.该方法利用逆向理论解决了散布熵的不足,能从原始信号中获得更多的故障特征.其次,从时间序列中分层提取高频和低频分量,可以提高对动态特征的描述能力.仿真和实验分别证明了层次逆向散布熵的优越性.实验结果表明,本文方法在提取故障信息方面明显优于多尺度逆向散布熵、多尺度排列熵和多尺度样本熵.
The fault information of rolling bearings is generally contained in vibration signals. How to effectively extract fault information from the original signals is the key to detecting and evaluating the health condition of mechanical equipment. In order to improve the accuracy of fault diagnosis, the hierarchical reverse dispersion entropy feature extraction method is proposed in this paper. This method solves the deficiency of spread entropy by using the reverse theory and can obtain more fault features from the original signals. Secondly, extracting high frequency and low frequency components from time series can improve the ability to describe dynamic features. Simulation and experiments respectively prove the superiority of the hierarchical reverse dispersion entropy. Experimental results show that the proposed method is superior to the multi-scale reverse dispersion entropy, the multi-scale permutation entropy and the multi-scale sample entropy in fault information extraction.
作者
谭力
李永健
王建景
孟威
林群煦
TAN Li;LI Yong-jian;WANG Jian-jing;MENG Wei;LIN Qun-xu(School of Rail Transportation,Wuyi University,Jiangmen 529020,China)
出处
《五邑大学学报（自然科学版）》
CAS
2023年第1期48-53,共6页
Journal of Wuyi University(Natural Science Edition)
