T
Thiago de M. Prego
Researcher at Centro Federal de Educação Tecnológica de Minas Gerais
Publications - 23
Citations - 217
Thiago de M. Prego is an academic researcher from Centro Federal de Educação Tecnológica de Minas Gerais. The author has contributed to research in topics: Reverberation & Random forest. The author has an hindex of 7, co-authored 20 publications receiving 143 citations. Previous affiliations of Thiago de M. Prego include Federal University of Rio de Janeiro.
Papers
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Journal ArticleDOI
Fault detection and classification in oil wells and production/service lines using random forest
Matheus Araújo Marins,Bettina D 'Avila Barros,Bettina D 'Avila Barros,Ismael Santos,Daniel C. Barrionuevo,Ricardo Emanuel Vaz Vargas,Thiago de M. Prego,Amaro A. de Lima,Marcello L. R. de Campos,Eduardo A. B. da Silva,Sergio L. Netto +10 more
TL;DR: An accuracy rate of 94% indicates a successful performance for the proposed system in detecting real events and the system’s time of detection was on average 12% of the transient period that precedes the fault steady-state.
Proceedings ArticleDOI
Blind estimators for reverberation time and direct-to-reverberant energy ratio using subband speech decomposition
TL;DR: Algorithms for estimating the reverberation time and direct-to-reverberant energy ratio are described, indicating the effectiveness of both techniques particularly in high-SNR situations.
Journal ArticleDOI
A blind algorithm for reverberation-time estimation using subband decomposition of speech signals.
Thiago de M. Prego,Amaro A. de Lima,Sergio L. Netto,Bowon Lee,Amir Said,Ronald W. Schafer,Ton Kalker +6 more
TL;DR: An algorithm for blind estimation of reverberation time (RT) in speech signals is proposed, achieving 91% and 97% correlation with the RTs measured by a standard nonblind method, indicating that the proposed method blindly estimates the RT in a reliable and consistent manner.
Proceedings ArticleDOI
On fault classification in rotating machines using fourier domain features and neural networks
A. A. de Lima,Thiago de M. Prego,Sergio L. Netto,E.A.B. da Silva,Ricardo H. R. Gutiérrez,Ulisses A. Monteiro,A. C. R. Troyman,Francisco J. da C Silveira,Luiz Felipe Hupsel Vaz +8 more
TL;DR: A classifier based on an artificial neural network is described, achieving a global accuracy rate of 93.5% in the problem of classifying mechanical faults in rotating machines.
Proceedings ArticleDOI
The influence of feature vector on the classification of mechanical faults using neural networks
Denys Pestana-Viana,Rafael Zambrano-Lopez,Amaro A. de Lima,Thiago de M. Prego,Sergio L. Netto,Eduardo A. B. da Silva +5 more
TL;DR: Two new signal features, namely the kurtosis and entropy, are considered along with main spectral peaks to discriminate between several machine conditions: normal operation, (vertical and horizontal) misalignment, unbalanced load and bearing faults.