Machinery vibration signal denoising based on learned dictionary and sparse representation

11th International Conference on Damage Assessment of Structures

物理学;材料科学

Guo, Liang^1 ; Gao, Hongli^1 ; Li, Jun^1 ; Huang, Haifeng^1 ; Zhang, Xiaochen^1

School of Mechanical Engineering, Southwest Jiaotong University, ChengDu

610031, China^1

关键词: Computing efficiency;    Efficiency calculations;    Mechanical vibration signals;    Online dictionary learning;    Orthogonal matching pursuit;    Real-time signal processing;    Sparse reconstruction;    Sparse representation;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/628/1/012124/pdf
DOI  :  10.1088/1742-6596/628/1/012124

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

Mechanical vibration signal denoising has been an import problem for machine damage assessment and health monitoring. Wavelet transfer and sparse reconstruction are the powerful and practical methods. However, those methods are based on the fixed basis functions or atoms. In this paper, a novel method is presented. The atoms used to represent signals are learned from the raw signal. And in order to satisfy the requirements of real-time signal processing, an online dictionary learning algorithm is adopted. Orthogonal matching pursuit is applied to extract the most pursuit column in the dictionary. At last, denoised signal is calculated with the sparse vector and learned dictionary. A simulation signal and real bearing fault signal are utilized to evaluate the improved performance of the proposed method through the comparison with kinds of denoising algorithms. Then Its computing efficiency is demonstrated by an illustrative runtime example. The results show that the proposed method outperforms current algorithms with efficiency calculation.