摘要
铁道车辆轮对轴承在故障发展的早期阶段,其振动信号中故障冲击成分比较微弱,容易淹没在轮轨冲击的强背景噪声中,在根据多点峭度谱周期区间最大值选择时总是选出干扰噪声周期而非故障周期,导致所提取信号中包含的故障信息较少,难以识别轴承故障。针对这一问题,提出了基于Teager能量算子的改进MOMEDA方法,采用Teager能量算子增强原始信号的冲击性和周期性,确保MOMEDA算法选取到精确的故障周期,进而准确提取轴承故障信息,同时引入周期误差率指标,用于衡量实际周期偏离理论周期的程度。通过仿真信号与货车轮对轴承试验及高铁轴承试验的验证,可以发现该方法提取故障信息的准确性较传统方法有了很大提升。研究结果对提升现有铁路轴承故障识别的准确率具有一定的理论和应用价值。
In early stage of fault development of railway vehicle wheel set bearings,the fault impact component in vibration signal is relatively weak,and it is easy to be submerged in strong background noise caused by wheel-rail impact.According to maximum value of multi-point kurtosis spectrum period interval,the interference noise period is always selected instead of fault period,resulting in less fault information in extracted signal and difficult identification of bearing fault.In order to solve the problem,an improved Multipoint Optimal Minimum Entropy Deconvolution Adjusted(MOMEDA)method based on Teager Energy Operator(TEO)is proposed.The TEO is adopted to enhance impact and periodicity of original signal,so as to ensure that the MOMEDA algorithm selects accurate fault period,and then extracts bearing fault information accurately.At the same time,the periodic error rate index is introduced to measure deviation degree of actual period from theoretical period.Through verification between simulation signal and truck wheel set bearing test and high speed railway bearing test,it is found that the accuracy of the method in extracting fault information is improved greatlycompared with traditional method.The research results have certain theoretical and practical value for improving accuracy of fault identification of railway bearings.
作者
乔志城
刘永强
廖英英
QIAO Zhicheng;LIU Yongqiang;LIAO Yingying(School of Mechanical Engineering,Shijiazhuang Tiedao University,Shijiazhuang 050043,China;School of Civil Engineering,Shijiazhuang Tiedao University,Shijiazhuang 050043,China;State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures,Shijiazhuang 050043,China)
出处
《轴承》
北大核心
2020年第4期43-50,共8页
Bearing
基金
国家自然科学基金项目(U1534204,11790282,11572206,11802184)
河北省自然科学基金项目(A2016210099)
河北省人才工程培养经费资助科研项目(A2016002036)。
