CAJ | 학술논문

为了从齿轮故障信号中提取出包含故障信号的特征频率，提出了基于EEMD自适应形态学解调方法。首先采用EEMD （集合经验模式分解）进行降噪，将原始信号与不同的白噪声叠加组成目标信号，然后将目标信号分解为有限个IMF分量，选取主要信息求和重构，再用形态学滤波器提取故障信号的特征频率。针对形态学结构元素尺寸的选择问题，利用遗传算法来优化形态学结构元素，自适应寻求最优解。通过数字仿真试验和齿轮故障模拟实验，并与EMD （经验模式分解）、SVD（奇异值分解）方法进行了比较，结果表明该算法要优于其他两种方法，能够清晰地提取出故障信号的各种频率特征。
위료종치륜고장신호중제취출포함고장신호적특정빈솔，제출료기우EEMD자괄응형태학해조방법。수선채용EEMD （집합경험모식분해）진행강조，장원시신호여불동적백조성첩가조성목표신호，연후장목표신호분해위유한개IMF분량，선취주요신식구화중구，재용형태학려파기제취고장신호적특정빈솔。침대형태학결구원소척촌적선택문제，이용유전산법래우화형태학결구원소，자괄응심구최우해。통과수자방진시험화치륜고장모의실험，병여EMD （경험모식분해）、SVD（기이치분해）방법진행료비교，결과표명해산법요우우기타량충방법，능구청석지제취출고장신호적각충빈솔특정。
In order to extract the characteristic frequencies from gear fault signals containing fult information,the adaptive morphology demodulation method was proposed based on EEMD. EEMD (ensemble empirical mode decomposition)was used to reduce noise firstly,an original signal with superposition of different white noises formed target signals,and then the target signal was decomposed into a finite number of IMF components,the IMFs containing fault information were chosen and summed to reconstruct a signal.The morphologic filter was used to extract the characteristic frequencies containing fault information from the reconstructed signal.Aiming at the problem of morphologic structural element size selection,the genetic algoritms was used to adoptirely optimize the structural elements of morphology. Through numerical simulations and gear fault simulation tests,the proposed method was compared with EMD and SVD. The results showed that the proposed method is superior to the other two,it can be used to clearly extract various characteristic frequencies of gear faults.