Singularity analysis of the vibration signals by means of wavelet modulus maximal method

By Zhike Peng, F. Chu, P. W. Tse. 2007

87 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Control and Systems Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/685d2887486d623e72da55d1871ba0dac551ea5f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Robert X. Gao
2. Da‐Wen Sun
3. Ruqiang Yan
4. Xuefeng Chen
5. M. Kamruzzaman

In Control and Systems Engineering

1. Ruqiang Yan
2. Xuefeng Chen
3. Robert X. Gao
4. Da‐Wen Sun
5. Hongbin Pu
6. Bin Jiang
7. Hongtian Chen
8. Qingbo He

In Mechanics of Materials

1. Xuefeng Chen
2. Ruqiang Yan
3. Robert X. Gao
4. Da‐Wen Sun
5. Bin Jiang
6. Hongtian Chen

Citations in the corpus

• Wavelets for fault diagnosis of rotary machines: A review with applications (2014, methodology): “to characterize a signal's singularity [26], and three parameters (the number of Lipschitz exponents per rotation, the mean value and the relative standard deviation of Lipschitz exponents) extracted from themwere used to differentiate four typical rotary machine faults: rub-impact between stator an”
• Application of Wavelet Analysis to Spectral Data for Categorization of Lamb Muscles (2014, background): “According to the theory of modulus maximum (Peng et al. 2007), it was found that wavelet coefficients around 1,680 nm decreased with the increasing decomposition level of wavelet transform.”