Weak Feature Signals Identification Method Based on Local Projective and Wavelet Transform
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摘要: 綜合局部投影算法及小波變換兩者的優點,提出了基于局部投影和小波降噪的弱沖擊信號的提取方法.實驗結果表明,局部投影算法可以將背景信號和特征信號分解到不同的子空間上,小波降噪可以有效地用于包含尖峰或突變信號的降噪,結合局部投影和小波降噪的弱沖擊信號的提取方法對于微弱特征信號的提取是非常有效的.Abstract: A weak feature signals identification method based on local projection and wavelet transform is introduced. Experiment indicates that the local projective algorithm can separate background signals and weak feature signals into different orthogonal sub-spaces. Wavelet transform is effective for noise reduction of sharp and break signals. The algorithm which combines the local projective and wavelet transform has an excellent effect on identifying weak feature signals in nonlinear time series.
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Key words:
- local projective /
- weak feature signal /
- wavelet transform /
- nonlinear time series /
- fault diagnosis
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