CAJ | 학술논문

经验模态分解(EMD)是希尔伯特?黄变换(HHT)中的关键步骤,并伴有过冲和端点效应的产生.利用遗传算法(GA)对支持向量机(SVM)中的未知参数:惩罚函数 C和高斯核函数中的预设参数σ进行优化选取,运用 GA-SVM 对信号进行端点延拓来处理端点效应问题并提出采用分段三次 Hermite多项式插值进行包络线拟合;为了机械设备早期故障频率的特征提取,采用小波包降噪预处理,结合改进的 Hilbert?Huang变换进行轴承故障特征频率的提取实验;实验表明该方法提高了故障频率提取的准确性.
경험모태분해(EMD)시희이백특?황변환(HHT)중적관건보취,병반유과충화단점효응적산생.이용유전산법(GA)대지지향량궤(SVM)중적미지삼수:징벌함수 C화고사핵함수중적예설삼수σ진행우화선취,운용 GA-SVM 대신호진행단점연탁래처리단점효응문제병제출채용분단삼차 Hermite다항식삽치진행포락선의합;위료궤계설비조기고장빈솔적특정제취,채용소파포강조예처리,결합개진적 Hilbert?Huang변환진행축승고장특정빈솔적제취실험;실험표명해방법제고료고장빈솔제취적준학성.
Empirical Mode Decomposition(EMD) decomposition is a critical step in Hilbert-Huang Transform(HHT), accompanied by overshoot and endpoint effect. The Genetic Algorithm(GA) is used to optimize and select the unknown parameters including the penalty function C and default parametersσof Gaussian kernel of Support Vector Machines(SVM). GA-SVM is applied to extend signals to deal with endpoint effect, and cubic Hermite polynomial interpolation is adopted for envelope fitting. In order to extract the early stage fault frequency features of mechanical equipment, wavelet packet noise reduction pretreatment is performed, combined with the extraction experiment of bearing fault feature frequency by using improved HHT transform. The experimental results show that the proposed method can improve the accuracy of fault frequency extraction.