Damage and nonlinearities detection in wind turbine blades based on strain field pattern recognition. FBGs, OBR and strain gauges comparison

By J. Sierra-Pérez, M. Torres-Arredondo, A. Güemes. 2016

141 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Computer Vision and Pattern Recognition, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/70e1c37f83b8336df8c45fc39a79a186fa4b6d7f

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. António Barrias
2. Sergi Villalba
3. Joan Ramon Casas
4. Hadi Salehi
5. Rigoberto Burgueño
6. Luca Palmieri
7. Luca Schenato

In Computer Vision and Pattern Recognition

1. Hadi Salehi
2. Rigoberto Burgueño
3. Sergi Villalba
4. António Barrias
5. Joan Ramon Casas

In Mechanical Engineering

1. Hadi Salehi
2. Rigoberto Burgueño
3. Sergi Villalba
4. António Barrias
5. Joan Ramon Casas

Citations in the corpus

• A Review of Distributed Optical Fiber Sensors for Civil Engineering Applications (2016, methodology): “Example of strain profiles for seven load magnitudes gathered with the OBR, the FBGs and the strain gauges [98].”
• Emerging artificial intelligence methods in structural engineering (2018, methodology): “[109] Damage detection Wind turbine blades Hierarchical nonlinear principal component analysis”