CAJ | 학술논문

齿轮故障信号具有非线性、非平稳特征，齿轮发生故障时，信号的能量结构随之改变，在不同的频带内能量不同。传统方法采用局部均值分解(LMD)提取振动信号的能量熵，将能量熵指标作为故障评判标准进行故障分类，依靠单一传感器信息源进行故障诊断，因而容易造成误诊、漏诊。全矢 LMD能量熵法融合了双通道同源信息的回转能量，可降低故障误判率。通过实验模拟齿轮正常、齿根裂纹、断齿、缺齿等4种状态，验证了全矢 LMD 能量熵作为故障特征能达到很好的故障分类效果。
치륜고장신호구유비선성、비평은특정，치륜발생고장시，신호적능량결구수지개변，재불동적빈대내능량불동。전통방법채용국부균치분해(LMD)제취진동신호적능량적，장능량적지표작위고장평판표준진행고장분류，의고단일전감기신식원진행고장진단，인이용역조성오진、루진。전시 LMD능량적법융합료쌍통도동원신식적회전능량，가강저고장오판솔。통과실험모의치륜정상、치근렬문、단치、결치등4충상태，험증료전시 LMD 능량적작위고장특정능체도흔호적고장분류효과。
Gear vibration signals in the events of failure were often non-stationary,non-linear.En-ergy structure would change in the fault signals,leading to different energy in different frequency bands.LMD was used to extract energy entropy of the vibration signals,and energy entropy was used as failure evaluation standards for fault classification.It is easy to be misdiagnosed with the traditional single channel signal diagnostic method.Full vector LMD energy entropy integrated two-channel ho-mologous informations of vibration signals,and reduced the misdiagnosis rate.Through experiments the gear normal state,tooth root crack,broken teeth,missing teeth were simulated,and it is proved that with full vector LMD energy entropy as fault feature can achieve good fault classification results.