Damage identification for beams in noisy conditions based on Teager energy operator-wavelet transform modal curvature

By Maosen Cao, Wei Xu, Wieslaw Ostachowicz, Z. Su. 2014

127 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Computer Vision and Pattern Recognition, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/2de29f74b98ea1613fdce916945528b362b29e84

How researchers see this paper

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

In Civil and Structural Engineering

1. Walter Lacarbonara
2. Giuseppe Quaranta
3. Hongwei Ma
4. Zhen-Hua Nie
5. Yi-Zhou Lin
6. Biagio Carboni

In Computer Vision and Pattern Recognition

1. Walter Lacarbonara
2. Giuseppe Quaranta
3. Hongwei Ma
4. Zhen-Hua Nie
5. Yi-Zhou Lin
6. Biagio Carboni

In Mechanics of Materials

1. Walter Lacarbonara
2. Giuseppe Quaranta
3. Biagio Carboni
4. Kim-Meow Liew
5. Z. Lei

Citations in the corpus

• Structural Damage Detection with Automatic Feature‐Extraction through Deep Learning (2017, background): “The problem of the susceptibility to measurement noise of the modal cur-vatures was also discussed (Cao et al., 2014).”
• Damage detection by modal curvatures: numerical issues (2016, background): “In contrast, a trade-off for the scale is required to tackle noisy data through continuous wavelet transforms (Gentile and Messina, 2003), and thus curvatures have to be observed within a multiscale space (Cao et al., 2014).”