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基于WPD-FWEO的轴承故障特征增强方法
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国家自然科学基金资助项目(51665014); 江西省重点研发项目(20181BEB50016); 广西2018年度第二批职业教育示范特色专业及实训基地项目


Fault Feature Enhancement Method for Rolling Bearing Based on WPD-FWEO
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    摘要:

    用小波包分解(Wavelet Packet Decomposition, WPD)处理低信噪比信号时,常出现残存大量带内噪声的问题,严重影响了后期的故障诊断准确性。针对该问题,提出将频率加权能量算子(Frequency-Weighted Energy Operator, FWEO)作为小波包分解的后处理器,以消除其带内噪声,增强故障特征提取效果。对采样获得的故障数据进行3层小波包分解,得到各频带系数;对每个频带系数进行峭度计算,以峭度最大原则获取最优频带系数;以频率加权能量算子追踪最优频带系数的瞬时能量,从信号能量的角度消除信号中的带内噪声成分,二次增强信号中隐藏的故障脉冲信息;对其进行包络谱分析,得到最终诊断结果。仿真数据、实验室数据和工程数据验证了所提方法的有效性和实用性。

    Abstract:

    There are lots of inband noise left behind when wavelet packet decomposition (WPD) is used to process signal with low SNR, which seriously affects the accuracy of rolling bearing fault diagnosis. Aimed at this issue, the frequencyweighted energy operator (FWEO) was proposed as a postprocessor of WPD to eliminate the inband noise and enhance the effect of fault feature extraction. The three layers WPD was carried out on the sampled fault data, and the coefficients of each frequency band were obtained. The kurtosis of each frequency band coefficient was calculated to obtain the optimal frequency band coefficient based on the principle of kurtosis maximization. The FWEO was used to track the instantaneous energy of the optimal frequency band coefficient, the inband noises was eliminated and fault impulse response information was further enhanced from the view point of signal energy. The final diagnosis results were obtained by analyzing the envelope spectrum. The effectiveness and practicability of the proposed method were verified through simulation data, laboratory data and engineering data.

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丁金玲,胡俊锋,林凤涛.基于WPD-FWEO的轴承故障特征增强方法[J].机床与液压,2020,48(11):194-199.
. Fault Feature Enhancement Method for Rolling Bearing Based on WPD-FWEO[J]. Machine Tool & Hydraulics,2020,48(11):194-199

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  • 在线发布日期: 2020-08-21
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