Weak Feature Signals Identification Method Based on Local Projective and Wavelet Transform

LU Yong; XU Jinwu; LI Yourong; YANG Debin

doi:10.13374/j.issn1001-053x.2004.03.024

Volume 26 Issue 3

Aug. 2021

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LU Yong, XU Jinwu, LI Yourong, YANG Debin. Weak Feature Signals Identification Method Based on Local Projective and Wavelet Transform[J]. Chinese Journal of Engineering, 2004, 26(3): 319-321. doi: 10.13374/j.issn1001-053x.2004.03.024

Citation:

LU Yong, XU Jinwu, LI Yourong, YANG Debin. Weak Feature Signals Identification Method Based on Local Projective and Wavelet Transform[J]. Chinese Journal of Engineering, 2004, 26(3): 319-321. doi: 10.13374/j.issn1001-053x.2004.03.024

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Weak Feature Signals Identification Method Based on Local Projective and Wavelet Transform

doi: 10.13374/j.issn1001-053x.2004.03.024

1. Mechanical Engineering School, University of Science and Technology Beijing, Beijing 100083, China;
2. Mechanical Engineering School, Wuhan University of Science and Technology Wuhan 430081, China

Received Date: 2003-04-28
Available Online: 2021-08-16

Abstract

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.
- local projective,
- weak feature signal,
- wavelet transform,
- nonlinear time series,
- fault diagnosis