Bearing fault diagnosis based on EMD-KPCA and ELM

摘要：

　　In recent years, many studies have been conducted in bearing fault diagnosis, which has attracted increasing attention due to its nonlinear and non-stationary characteristics.To solve this problem, this paper proposes, a fault diagnosis method based on Empirical Mode Decomposition (EMD), Kernel Principal Component Analysis (KPCA), and Extreme Learning Machines (ELM) neural network, which combines the existing self-adaptive time-frequency signal processing with the advantages of non-linear multivariate dimensionality reduction KPCA approach and ELM neural network.First, EMD is applied to decompose the vibration signals into a finite number of intrinsic mode functions, in which the corresponding energy values are selected as the initial feature vector.Second, KPCA is used to further reduce the dimensionality for a simplified low-dimension feature vector.Finally, ELM is introduced to classify the extracted fault feature vectors for lessening the human intervention and reducing the fault diagnosis time.Experimental results demonstrate that the proposed diagnostic can effectively identify and classify typical bearing faults.

关键词： fault diagnosis empirical mode decomposition kernel principal component analysis extreme learning machines

作者: Zihan Chen Hang Yuan

作者单位: School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China Science and Technology on Reliability & Environmental Engineering Laboratory, Beijing 100191, China

会议类型: 国际会议

会议名称: the International Conference Vibroengineering-2014

会议地点: 贵阳

会议语种:英文

页码: 200-205

在线出版日期: 2014-11-07（万方平台首次上网日期，不代表论文的发表时间）