MULTIWAVELET DENOISING WITH NEIGHBORING COEFFICIENTS FOR APPLICATION IN ROLLING BEARING FAULT DIAGNOSIS
Multiwavelets have been formulated recently by using translates and dilates of more than one mother wavelet function. Multiwavelets offer simultaneously orthogonality, symmetry, and compact support, which is impossible for scalar wavelets. This leads to better performance in signal denoising. In this paper, the effects of various prefilters on denoising are compared and the optimal method is chosen. Discrete multiwavelet transform is integrated with neighboring coefficient denoising, which performs better in extracting feature of impulse signal. We apply this approach to fault diagnosis of rolling bearings on electric locomotive, obtaining performance superior to that using scalar wavelets with hard thresholding or soft thresholding.
Multiwavelets Signal Denoising Neighboring Coefficients Bearing Fault Diagnosis
Xiaodong Wang Zhengjia He Yanyang Zi
School of Mechanical Engineering, Xian Jiaotong University, Xian 710049, PR China. School of Mechanical Engineering, Xian Jiaotong University, Xian 710049, PR China;State Key Labora
国际会议
北京
英文
1730-1740
2008-10-27（万方平台首次上网日期，不代表论文的发表时间）