| SIGNAL FEATURE EXTRACTION BASED UPON INDEPENDENT COMPONENT ANALYSIS AND WAVELET TRANSFORM |
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| 引用本文： | Ji Zhong Jin Tao Qin Shuren College of Mechanical Engineering,Chongqing University,Chongqing 400030,China. SIGNAL FEATURE EXTRACTION BASED UPON INDEPENDENT COMPONENT ANALYSIS AND WAVELET TRANSFORM[J]. 机械工程学报(英文版), 2005, 18(1): 123-126 |
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| 作者姓名： | Ji Zhong Jin Tao Qin Shuren College of Mechanical Engineering Chongqing University Chongqing 400030 China |
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| 作者单位： | Ji Zhong Jin Tao Qin Shuren College of Mechanical Engineering，Chongqing University，Chongqing 400030，China |
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| 基金项目： | This project is supported by National Natural Science Foundation of China (No.50275154) Municipal Natural Science Foundation of Chongqing, China (No.8773). |
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| 摘 要： | It is an important precondition for machine fault diagnosis that vibration signal can be extracted effectively. Based on the characteristic of noise interfused during the course of sampling vibration signal, independent component analysis (ICA) method is combined with wavelet to de-noise. Firstly, The sampled signal can be separated with ICA, then the function of frequency band chosen with multi-resolution wavelet transform can be used to judge whether the stochastic disturbance singular signal is interfused. By these ways, the vibration signals can be extracted effectively, which provides favorable condition for subsequent feature detection of vibration signal and fault diagnosis.
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| 关 键 词： | 独立成分 小波变换 噪声故障诊断 特征提取 旋钻式机械 |
SIGNAL FEATURE EXTRACTION BASED UPON INDEPENDENT COMPONENT ANALYSIS AND WAVELET TRANSFORM |
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| JiZhong JinTao QinShuren. SIGNAL FEATURE EXTRACTION BASED UPON INDEPENDENT COMPONENT ANALYSIS AND WAVELET TRANSFORM[J]. Chinese Journal of Mechanical Engineering, 2005, 18(1): 123-126 |
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| Authors: | JiZhong JinTao QinShuren |
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| Affiliation: | CollegeofMechanicalEngineering,ChongqingUniversity,Chongqing400030,China |
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| Abstract: | It is an important precondition for machine fault diagnosis that vibration signal can be extracted effectively. Based on the characteristic of noise interfused during the course of sampling vibration signal, independent component analysis (ICA) method is combined with wavelet to de-noise. Firstly, The sampled signal can be separated with ICA, then the function of frequency band chosen with multi-resolution wavelet transform can be used to judge whether the stochastic disturbance singular signal is interfused. By these ways, the vibration signals can be extracted effectively, which provides favorable condition for subsequent feature detection of vibration signal and fault diagnosis. |
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| Keywords: | Independent component analysis (ICA) Wavelet transform De-noising Fault diagnosis Feature extraction |
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