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FAULT FEATURE EXTRACTION FOR ROLLING BEARING BASED ON DUAL IMPULSE MORLET WAVELET

机译:基于双脉冲Morlet小波的滚动轴承故障特征提取

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摘要

The common impulse feature model is oscillating and attenuated signal with a single maximum peak, while the formation principle of actual impulse feature is ignored. Considering the continuous dual impulse waveform feature of rolling bearing fault in the vibration signal and the matching effects of the single impulse waveform with Morlet wavelet, a "dual impulse Morlet wavelet" model is proposed. Through ant colony algorithm with the indicator of the maximum cross-correlation, 4 types of parameters are optimized adaptively which affect the similar degree between dual impulse Morlet wavelet and the dual impulse waveform intercepted from the bearing vibration signal. Then, the optimal model is obtained. The bearing fault experiment verification shows that the optimal dual impulse Morlet wavelet can effectively improve the analytical precision and energy concentration of impulse feature in both of time domain and frequency domain, which overcomes the disadvantages of Morlet wavelet effectively.
机译:常见的脉冲特征模型是具有单个最大峰值的振荡和衰减信号,而忽略了实际脉冲特征的形成原理。考虑到振动信号中滚动轴承故障的连续双脉冲波形特征和单脉冲波形与Morlet小波的匹配效果,提出了“双脉冲Morlet小波”模型。通过具有最大互相关指标的蚁群算法,自适应地优化了影响双冲动莫雷特小波和轴承振动信号截获的双冲动波形之间相似度的四种参数。然后,获得最佳模型。轴承故障实验验证表明,最优的双脉冲Morlet小波可以在时域和频域上有效地提高脉冲特征的分析精度和能量集中度,有效克服了Morlet小波的弊端。

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