钻削振动信号小波包分频谱减特征增强方法
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TH166

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(国家自然科学基金资助项目(51775468,51375419,51375418);湘潭大学海泡石专项资助项目)


Feature Enhancement Method for Drilling Vibration Signal by Using Wavelet Packet Division-Spectral SubtrAction
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    摘要:

    针对在钻削加工噪声背景下振动信号微弱特征识别和提取困难的问题,提出了一种基于小波包分频谱减的钻削振动信号特征增强方法。首先,在经典谱减法原理的基础上,将钻削前机床空转信号视为监测信号的“加性噪声”;其次,根据钻削过程振动信号的特点,采用小波包分解方法将“加性噪声”和监测信号分成多个子频带;最后,对每个子频带内“加性噪声”的相应频带进行谱减处理后,重构钻削振动信号。仿真和实验结果表明,该方法能有效降低环境噪声对钻削过程特征提取的影响,从而达到增强监测信号特征的目的,同时建立了钻削过程与监测信号之间很好的映射关系模型。

    Abstract:

    In the light of the extraction of drilling process features of vibration signal from background noise, a new method is developed to enhance the signal feature by the wavelet packet division-spectral subtraction. At first, the machine’s idling signal is regarded as the“additive noise”of the monitoring signal according to the principle of classical spectral subtraction. Then, the“additive noise”and monitoring signal are divided into multiple sub-bands by the wavelet packet spectrum subtraction on the basis of the characteristics of vibration signal in the drilling process. Finally, the frequency spectrum of“additive noise”is subtracted from the corresponding sub-bands, and the vibration signal is reconstructed to enhance the drilling process feature. Simulations and experimental results show that the method can be used to enhance drilling process features of the monitoring vibration signal, to reduce the influence of background noise and to establish the mapping model between drilling process and monitoring signal.

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  • 在线发布日期: 2019-01-06
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