基于PPLCD方法的电机轴承故障振动信号特征提取  

Vibration Signal Feature Extraction of Motor Bearing Fault based on PPLCD Method

作　　者：李静[1] 袁志奇 Li Jing;Yuan Zhiqi(School of Intelligent Manufacturing,Xinxiang Vocational and Technical College,Xinxiang 453006,China)

机构地区：[1]新乡职业技术学院智能制造学院,河南新乡453006

出　　处：《防爆电机》2024年第5期10-12,共3页Explosion-proof Electric Machine

摘　　要：为了进一步提高电机轴承振动信号故障识别能力,设计了一种分段结构局部特征尺度分解(PPLCD)方法对故障振动信号特征进行提取。采用优化插值包络PPLCD方法并与LCD、K-C组合权重分量筛选方式相结合。研究结果表明:外圈重构信号故障频率是230.7 Hz,此时轴承中已经形成了外圈故障。PPLCD除了包含明显倍频以外,在2~3倍频形成了更大幅值,相对LCD表现出了更优性能,对于复杂以及强噪声干扰参数具备更强故障特征提取能力。该研究有助于提高电机运行过程中的故障提前排斥能力,具有很大的应用价值。In order to further improve the fault identification ability of vibration signal of motor bearing,a piecewise party-ensemble local characteristic-scale decomposition(PPLCD)method is designed to extract the fault vibration signal features.The optimal interpolation envelope PPLCD method is adopted and combined with the LCD and K-C combination weight component screening method.The research results show that the failure frequency of the reconstructed signal of the outer ring is 230.7 Hz,and the outer ring fault has been formed in the bearing at the moment.In addition to the obvious frequency doubling,PPLCD forms a larger amplitude at 2 to 3 frequency doubling,which shows that it has better performance than that of LCD,and has stronger fault feature extraction ability for the complex and strong noise interference parameters.This research is helpful to improve the failure rejection ability in advance during motor operation,and has a great application value.

关 键 词：轴承故障 特征提取 故障诊断 分段结构 

分 类 号：TM301.2[电气工程—电机]