Luis Fernando Pedrozo Melegari
Luis Fernando Pedrozo Melegari has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Beatriz Brusamarello
Graduate Program in Electrical and Computer Engineering (CPGEI-UTFPR), Curitiba 80230-901, Brazil
1 shared publication - Cicero Martelli
Graduate Program in Electrical and Computer Engineering, Universidade Tecnolgica Federal do Paran, Curitiba, Brazil
1 shared publication - Gilson Antonio Brunetto
CPFL Energia, Campinas 13076-970, Brazil
1 shared publication - Jean Carlos Cardozo da Silva
Universidade Tecnológica Federal do Paraná
1 shared publication - Uilian José Dreyer
Universidade Tecnológica Federal do Paraná
1 shared publication
Latest Publications
- Open Access
Multilayer Structure Damage Detection Using Optical Fiber Acoustic Sensing and Machine Learning
Over the past decade, distributed acoustic sensing has been utilized for structural health monitoring in various applications, owing to its continuous measurement capability in both time and space and its ability to deliver extensive data on the conditions of large structures using just a single optical cable. This work aims to evaluate the performance of distributed acoustic sensing for monitoring a multilayer structure on a laboratory scale. The proposed structure comprises four layers: a medium-density fiberboard and three rigid polyurethane foam slabs. Three different damages were emulated in the structure: two in the first layer of rigid polyurethane foam and another in the medium-density fiberboard layer. The results include the detection of the mechanical wave, comparing the response with point sensors used for reference, and evaluating how the measured signal behaves in time and frequency in the face of different damages in the multilayer structure. The tests demonstrate that evaluating signals in both time and frequency domains presents different characteristics for each condition analyzed. The supervised support vector machine classifier was used to automate the classification of these damages, achieving an accuracy of 93%. The combination of distributed acoustic sensing with this learning algorithm creates the condition for developing a smart tool for monitoring multilayer structures.
Open AccessMultilayer Structure Damage Detection Using Optical Fiber Acoustic Sensing and Machine Learning
Over the past decade, distributed acoustic sensing has been utilized for structural health monitoring in various applications, owing to its continuous measurement capability in both time and space and its ability to deliver extensive data on the conditions of large structures using just a single optical cable. This work aims to evaluate the performance of distributed acoustic sensing for monitoring a multilayer structure on a laboratory scale. The proposed structure comprises four layers: a medium-density fiberboard and three rigid polyurethane foam slabs. Three different damages were emulated in the structure: two in the first layer of rigid polyurethane foam and another in the medium-density fiberboard layer. The results include the detection of the mechanical wave, comparing the response with point sensors used for reference, and evaluating how the measured signal behaves in time and frequency in the face of different damages in the multilayer structure. The tests demonstrate that evaluating signals in both time and frequency domains presents different characteristics for each condition analyzed. The supervised support vector machine classifier was used to automate the classification of these damages, achieving an accuracy of 93%. The combination of distributed acoustic sensing with this learning algorithm creates the condition for developing a smart tool for monitoring multilayer structures.