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Condition Feature Extraction of Machine Tools Based on Wavelet Packet Energy Spectrum Analysis and Bispectrum Analysis of Current Signal

机译:基于小波分组能谱分析的机床功能提取和电流信号的BISPectrum分析

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Based on the study of the characteristics of load current signal, this article develops a method to extract features that can be use to distinguish the different working status of machine tools in real-time manner. The features are extracted from wavelet packet energy spectrum and bispectrum of the load current signal, and thus can take advantages of both wavelet packet transforms and bispectrum in signal analysis. Experimental results show that, compared with the features extracted from wavelet packet energy spectrum or bispectrum alone, the features extracted by applying the proposed method can provide better performance in term of identifying the machine working status.
机译:基于对负载电流信号特性的研究,本文开发了一种提取了可以用于区分机床的不同工作状态以实时方式来提取的方法的方法。从小波分组能谱和负载电流信号的BIS谱中提取的特征,因此可以采用小波包变换和BISPectrum在信号分析中的优点。实验结果表明,与单独的小波分组能谱或双谱提取的特征相比,通过施加所提出的方法提取的特征可以在识别机器工作状态方面提供更好的性能。

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