KPCA denoising and its application in machinery fault diagnosis

摘要：

This paper proposes a kernel principal component analysis (KPCA)-based denoising method for removing the noise from vibration signal. Firstly, onedimensional time series is expanded to multidimensional time series by the phase space reconstruction method. Then, KPCA is performed on the multidimensional time series. The first kernel principal component is the denoised signal. A rolling bearing denoising example verify the effectiveness of the proposed method

关键词： KPCA denoising fault diagnosis rotating machinery

作者: Ling-Li Jiang Zong-Qun Deng Si-Wen Tang

作者单位: Engineering Research Center of Advanced Mining Equipment, Ministry of Education,Hunan University of Hunan Provincial Key Laboratory of Health Maintenance for Mechanical Equip-ment, Hunan University of

会议类型: 国际会议

会议名称: The 3th International Conference on Precision Instrumentation and Measurement 2011(CPIM2011)(第三届精密仪器与测量国际学术会议)

会议地点: 湘潭

会议语种:英文

页码: 274-278

在线出版日期: 2011-07-19（万方平台首次上网日期，不代表论文的发表时间）