Condition Feature Extraction of Machine Tools Based on Wavelet Packet Energy Spectrum Analysis and Bispectrum Analysis of Current Signal
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...
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| Veröffentlicht in: | Applied Mechanics and Materials 2012-01, Vol.101-102, p.847-850 |
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| container_title | Applied Mechanics and Materials |
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| creator | Li, Guo Fu Fang, Teng Fei |
| description | 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. |
| doi_str_mv | 10.4028/www.scientific.net/AMM.101-102.847 |
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| title | Condition Feature Extraction of Machine Tools Based on Wavelet Packet Energy Spectrum Analysis and Bispectrum Analysis of Current Signal |
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