CAJ | 학술논문

提出了一种基于掩蔽经验模式分解(MEMD)互近似熵及模糊C均值聚类(FCM)的滚动轴承故障诊断新方法.MEMD可以有效抑制经验模式分解存在的模态混叠问题；互近似熵是近似熵的改进，能更好体现信号的不规则度和复杂度.信号经掩蔽经验模式分解后得到一组平稳的本征模函数(IMF)，通过能量分析筛选出与原始信号最为相关的几个 IMF 分量，计算其互近似熵值以作为故障特征向量，能够直观体现设备的运行状况.故障模式识别采用的 FCM算法，计算相对简单，聚类效果好.实验分析证明了该方法的优越性.
제출료일충기우엄폐경험모식분해(MEMD)호근사적급모호C균치취류(FCM)적곤동축승고장진단신방법.MEMD가이유효억제경험모식분해존재적모태혼첩문제；호근사적시근사적적개진，능경호체현신호적불규칙도화복잡도.신호경엄폐경험모식분해후득도일조평은적본정모함수(IMF)，통과능량분석사선출여원시신호최위상관적궤개 IMF 분량，계산기호근사적치이작위고장특정향량，능구직관체현설비적운행상황.고장모식식별채용적 FCM산법，계산상대간단，취류효과호.실험분석증명료해방법적우월성.
A new fault diagnosis method for rolling bearings was presented based on MEMD cAp-En and FCM clustering herein.The MEMD method could restrain mode mixing of EMD effectively and the cApEn was the improvement of approximate entropy,which could express the more irregular-ity and complexity.Signals were decomposed by MEMD to obtain a set of stationary intrinsic mode function (IMF),and some IMF components that were most relevant to the original signals were sifted out by the energy analysis criterion.The cApEn values of every IMF components were calculated as fault feature vectors that could represent the operating conditions of equipment more intuitively.FCM algorithm was introduced to fault recognition,which could achieve well effect of clustering with easier calculation.The experiments and engineering analyses demonstrate the superiority of this method.