基于综合KPC解卷积的机车轴承故障诊断方法  

Fault diagnosis method of locomotive bearing based on integrated KPC deconvolution

作　　者：陈之恒 邓聪 王诗航 仲继生 钱彦行 周奥 CHEN Zhiheng;DENG Cong;WANG Shihang;ZHONG Jisheng;QIAN Yanxing;ZHOU Ao(CRRC Zhuzhou Locomotive Co.,Ltd.,Zhuzhou 412001,China)

机构地区：[1]中车株洲电力机车有限公司,湖南株洲412001

出　　处：《电力机车与城轨车辆》2024年第5期30-36,共7页Electric Locomotives & Mass Transit Vehicles

摘　　要：针对电力机车轴箱轴承故障的诊断,文章提出了一种基于峭度、皮尔逊相关系数和故障冲击比(综合KPC)解卷积的轴箱轴承诊断方法。首先运用改进的互补集合经验模态分解(CEEMD)算法对原始振动信号进行降噪处理,然后应用多点最优最小熵反褶积调整(MOMEDA)算法对重构信号进行解卷积后和包络解调分析,最终实现轴承故障诊断。研究结果表明,该方法相比传统方法具有较高的辨识度,且能很好地识别出轴承的故障模式。Aiming at the fault diagnosis of axle box bearing of electric locomotive,this paper proposes a diagnosis method of axle box bearing based on kurtosis,pearson correlation coefficient and fault impact ratio(integrated KPC)deconvolution.Firstly,the original vibration signal is denoised by using improved complementary ensemble empirical mode decomposition(CEEMD)method.Then,the reconstructed signal is deconvoluted by multipoint optimal minimum entropy deconvolution adjusted(MOMEDA)method and envelope demodulation analysis is carried out,so as to realize bearing fault diagnosis.The research results show that this method has a higher degree of identification than traditional methods,and can identify the bearing fault mode well.

关 键 词：机车 故障诊断 轴箱轴承 解卷积 

分 类 号：U270.7[机械工程—车辆工程]