Rogério C. N. Rocha
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Rogério C. N. Rocha has a total of 8 co-authors. Here are the top co-authors by number of shared publications:
- Angélica Cidália Gouveia dos Santos
Graduate Program in Computational Modeling and Systems, State University of Montes Claros, Montes Claros CEP 39401-089, MG, Brazil
1 shared publication - Francesco Vidoli
University of Urbino Carlo Bo
1 shared publication - Laércio I. Santos
Campus Montes Claros, Federal Institute of Norte de Minas Gerais, Rua Dois, 300-Village do Lago I, Montes Claros 39404-058, Brazil
1 shared publication - Marcos Flávio Silveira Vasconcelos Dangelo
Universidade Estadual de Montes Claros, Montes Claros, Brazil
1 shared publication - Matheus Pereira Libório
Universidade Estadual de Montes Claros, Montes Claros, Brazil
1 shared publication - Murilo Osorio Camargos
Unimontes
1 shared publication
Latest Publications
- Open Access
A New Fault Classification Approach Based on Decision Tree Induced by Genetic Programming
This research introduces a new data-driven methodology for fault detection and isolation in dynamic systems, integrating fuzzy/Bayesian change point detection and decision trees induced by genetic programming for pattern classification. Tracking changes in sensor signals enables the detection of faults, and using decision trees generated by genetic programming allows for accurate categorization into specific fault classes. Change point detection utilizes a combination of fuzzy set theory and the Metropolis–Hastings algorithm. The primary contribution of the study lies in the development of a distinctive classification system, which results in a comprehensive and highly effective approach to fault detection and isolation. Validation is carried out using the Tennessee Eastman benchmark process as an experimental framework, ensuring a rigorous evaluation of the efficacy of the proposed methodology.
Open AccessA New Fault Classification Approach Based on Decision Tree Induced by Genetic Programming
This research introduces a new data-driven methodology for fault detection and isolation in dynamic systems, integrating fuzzy/Bayesian change point detection and decision trees induced by genetic programming for pattern classification. Tracking changes in sensor signals enables the detection of faults, and using decision trees generated by genetic programming allows for accurate categorization into specific fault classes. Change point detection utilizes a combination of fuzzy set theory and the Metropolis–Hastings algorithm. The primary contribution of the study lies in the development of a distinctive classification system, which results in a comprehensive and highly effective approach to fault detection and isolation. Validation is carried out using the Tennessee Eastman benchmark process as an experimental framework, ensuring a rigorous evaluation of the efficacy of the proposed methodology.