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- 저자 : Xiaoan Yan (검색결과 2 건)
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Weighted sparsity-based denoising for extracting incipient fault in rolling bearing
Wan Zhang,Minping Jia,Xiaoan Yan,Lin Zhu 대한기계학회 2017 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.31 No.10
Given that the incipient fault is too weak for extraction, a novel approach that is based on sparse optimization is proposed for incipient fault diagnosis. The proposed optimization method consists of three steps: First, autocorrelation analysis is utilized to filter broadband random noise. Then, the weighted sparsity-based denoising method is proposed to extract periodic impulses. The prior knowledge that periodic impulses are sparse is used to constitute a penalty term; thus a novel weighted sparse optimization model is established. The majorization-minimization method is used to solve the optimization model. The high-pass filter in quadratic fidelity term is constructed by a Butterworth filter based on banded matrices, thus effectively improving computational efficiency. Lastly, the interval of periodic impulses, which corresponds to the fault frequency of rolling bearing, is obtained. Moreover, simulation and experimental results show that the proposed approach can successfully extract fault features from the signals of low signal to noise ratio.
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Yifan Mao,Feiyun Xu,Xun Zhao,Xiaoan Yan 대한기계학회 2021 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.35 No.11
Gearbox vibration signals are usually disturbed under strong noise. Therefore, the fault feature frequency cannot be extracted accurately and effectively. In order to extract the fault feature of the gearbox, a method based on compound time-frequency atomic dictionary and orthogonal matching pursuit (OMP) optimized by wingsuit flying search algorithm (WFSA) is proposed. Firstly, according to the feature of the gearbox fault vibration signal, a compound time-frequency atomic dictionary composed of steady state modulation dictionary and impact modulation dictionary is designed. In addition, in order to improve the accuracy and efficiency of signal sparse decomposition. In this paper, WFSA is used to optimize the dictionary atomic parameters in OMP, so that the dictionary atom approximated the original signal better. Through simulation analysis and experimental verification and compared with the commonly used gearbox fault feature extraction methods, the superiority and effectiveness of the method are verified.
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