基于波形平均的经验模态分解端点效应抑制方法

发布时间：2018-04-28 17:02

本文选题：经验模态分解 + 端点效应　；参考：《机械设计与制造》2017年05期

【摘要】：经验模态分解(EMD)作为一种非常灵活的自适应时频分析方法,已广泛用于旋转机械故障诊断中的振动信号分析。但是,经验模态分解存在两个问题:端点效应以及模态混叠。针对EMD中存在的端点效应问题,在积分延拓局部均值分解(IELMD)的基础上,提出了一种利用波形平均来改进IELMD的方法。该方法利用一组相似波形来代替最佳匹配波形与特征波形相匹配,通过对相似波形左边或右边波形取平均得到延拓波形,将其附加在原始信号左端或右端。仿真和应用结果表明,与IELMD方法相比,该方法能够更有效地抑制经验模态分解端点效应。
[Abstract]:As a flexible adaptive time-frequency analysis method, empirical mode decomposition (EMD) has been widely used in vibration signal analysis of rotating machinery fault diagnosis. However, there are two problems in empirical mode decomposition: endpoint effect and modal aliasing. In order to solve the problem of endpoint effect in EMD, a method to improve IELMD by waveform averaging is proposed based on the integral continuation local mean decomposition (IELMD). In this method, a set of similar waveforms are used instead of the best matching waveforms to match the characteristic waveforms, and the continuation waveforms are obtained by averaging the left or right waveforms of the similar waveforms, which are attached to the left or right end of the original signals. The simulation and application results show that the proposed method is more effective than the IELMD method in suppressing the end point effect of empirical mode decomposition (EMD).
【作者单位】：四川中烟工业有限责任公司;西南科技大学信息工程学院;
【基金】：四川中烟工业有限责任公司公司科技项目-风机状态监控及故障趋势预测系统(川渝烟工技研[2015]62号)
【分类号】：TH17

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