基于衰减余弦字典和稀疏特征符号搜索算法的轴承微弱故障特征提取

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振动与冲击 ›› 2019, Vol. 38 ›› Issue (21) : 164-171.

论文

周浩轩，刘义民，刘韬

作者信息 +

Bearing weak fault feature extraction based on attenuated cosine dictionary and sparse feature sign search algorithm

ZHOU Haoxuan, LIU Yimin, LIU Tao

Author information +

文章历史 +

摘要

本文通过分析轴承内外圈故障时域波形特征，结合其早期微弱故障特点，提出了一种与轴承故障波形高度匹配的衰减余弦过完备字典，同时与稀疏表示基追踪方法的特征符号搜索算法相结合的新型算法（ACFS），实现了强噪声干扰下轴承微弱故障特征的提取。通过分析原始信号频谱与理论故障特征，确定了张成原子库的参数，并结合特征符号搜索算法对不同信噪比轴承内圈仿真信号和轴承全寿命数据中的早期微弱故障信号进行了分析。对比普通包络解调方法与基于Symlet8小波包字典的普通BPDN结果表明，该方法可以在极早期实际轴承故障信号中高效、准确地提取出故障特征频率。对于噪声具有极好的冗余度与鲁棒性。

Abstract

Here, through analyzing a bearing’s inner and outer ring faults’ time domain features and combining with its early weak fault features, a new algorithm was proposed using an attenuated cosine over-complete dictionary highly matching bearing fault waveforms, and combined with the sparse representation basis pursuing method’s sign search algorithm.It realized bearing weak fault feature extraction under interference of strong noise.The original signal’s spectrum and theoretical fault features were analyzed to determine parameters of the opened atomic library.Combining the atomic library and the feature sign search algorithm, early weak fault signals in bearing inner ring simulated signals with different signal to noise ratios and bearing whole life data were analyzed.Results of the common envelope demodulation method compared with those of the ordinary BPDN based on Symlet8 wavelet packet dictionary showed that the new algorithm can be used to effectively and correctly extract fault feature frequencies in very early actual bearing fault signals; it has excellent redundancy and robustness to noise.

导出引用

周浩轩，刘义民，刘韬. 基于衰减余弦字典和稀疏特征符号搜索算法的轴承微弱故障特征提取[J]. 振动与冲击, 2019, 38(21): 164-171

ZHOU Haoxuan, LIU Yimin, LIU Tao. Bearing weak fault feature extraction based on attenuated cosine dictionary and sparse feature sign search algorithm[J]. Journal of Vibration and Shock, 2019, 38(21): 164-171

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