摘要
为了对轴箱振动信息进行有效时频分析,提出利用完备集合经验模态(CEEMDAN)分解对振动信息进行分解,再通过Hilbert变换求其时频谱、边际谱和奇异值,实现振动信息的分析。通过分析钢轨正常和道岔状态轴箱振动信息,实现对钢轨表面状态的判断。结论如下:正常状态的振动频率主要集中在低频,道岔状态高频部分显著增加,且能量幅值峰值约是正常状态的50倍。道岔状态的边际谱和奇异值幅值均大于正常状态,且幅值峰值约是正常状态的3倍和5倍。
In order to carry out effective time-frequency analysis of axle box vibration information,a complete set empirical mode decomposition(CEEMDAN)is proposed to decompose the vibration information,and then the time spectrum,marginal spec⁃trum and singular value are obtained by Hilbert transform to realize the analysis of vibration information.By analyzing the axle box vibration information of normal rail and turnout state,the judgment of rail surface state is realized.The conclusions are as follows,the vibration frequency in the normal state is mainly concentrated in the low frequency,the high frequency part in the turnout state increases significantly,and the peak energy amplitude is about 50 times that in the normal section.The marginal spectrum and sin⁃gular value amplitude of turnout section are greater than those of normal section,and the peak amplitude is about 3 and 5 times that of normal section.
作者
陈钇行
钟倩文
崔桂艳
郑树彬
彭乐乐
李立明
CHEN Yihang;ZHONG Qianwen;CUI Guiyan;ZHENG Shubin;PENG Lele;LI Liming(School of Urban Railway Transportation,Shanghai University of Engineering Science,Shanghai 201620)
出处
《计算机与数字工程》
2023年第11期2719-2723,共5页
Computer & Digital Engineering
基金
国家自然科学基金项目(编号:51907117,51975347)
上海申通地铁集团项目(编号:JS-KY20R013-3)资助。
