Mateus Giesbrecht

@article{Rocha2022,

title = {Machine learning algorithms for dengue risk assessment: a case study for São Luís do Maranhão},

author = {Fernanda Paula Rocha and Mateus Giesbrecht},

doi = {10.1007/s40314-022-02101-z},

issn = {1807-0302},

year  = {2022},

date = {2022-12-00},

journal = {Comp. Appl. Math.},

volume = {41},

number = {8},

publisher = {Springer Science and Business Media LLC},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Cruz2022,

title = {Partial Discharges Monitoring for Electric Machines Diagnosis: A Review},

author = {Jonathan dos Santos Cruz and Fabiano Fruett and Renato da Rocha Lopes and Fabio Luiz Takaki and Claudia de Andrade Tambascia and Eduardo Rodrigues de Lima and Mateus Giesbrecht},

doi = {10.3390/en15217966},

issn = {1996-1073},

year  = {2022},

date = {2022-11-00},

journal = {Energies},

volume = {15},

number = {21},

publisher = {MDPI AG},

abstract = {Online monitoring of Partial Discharges (PDs) in rotating electrical machines is an useful tool for machine prognosis, as it presents reduced costs compared to intrusive inspections and is associated with relevant problems. Although this monitoring method has been developed for almost 50 years, the recent advancements in processes automation and signal processing techniques allow improvements that are still being studied by academic and industrial researchers. To analyze the current context of PDs monitoring, this article presents a literature review based on concepts of PDs in rotating machines, data acquisition techniques, state-of-the art commercial equipment, and recent methodologies for detection and pattern recognition of PDs. The challenges identified in the literature that motivate the development of more reliable and robust PD monitoring systems are presented and discussed.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Alegria2022,

title = {An associative-memory-based method for system nonlinearities recursive estimation},

author = {Elvis Jara Alegria and Mateus Giesbrecht and Celso Pascoli Bottura},

doi = {10.1016/j.automatica.2022.110343},

issn = {0005-1098},

year  = {2022},

date = {2022-08-00},

journal = {Automatica},

volume = {142},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Martins2021,

title = {Clearness index forecasting: A comparative study between a stochastic realization method and a machine learning algorithm},

author = {Guilherme Santos Martins and Mateus Giesbrecht},

doi = {10.1016/j.renene.2021.08.094},

issn = {0960-1481},

year  = {2021},

date = {2021-12-00},

journal = {Renewable Energy},

volume = {180},

pages = {787--805},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Gonçalves2021,

title = {Faults detection and classification in a centrifugal pump from vibration data using markov parameters},

author = {João Paulo S. Gonçalves and Fabiano Fruett and José G. Dalfré Filho and Mateus Giesbrecht},

doi = {10.1016/j.ymssp.2021.107694},

issn = {0888-3270},

year  = {2021},

date = {2021-09-00},

journal = {Mechanical Systems and Signal Processing},

volume = {158},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Alegria2021,

title = {Causal regression for online estimation of highly nonlinear parametrically varying models},

author = {Elvis Jara Alegria and Mateus Giesbrecht and Celso Pascoli Bottura},

doi = {10.1016/j.automatica.2020.109425},

issn = {0005-1098},

year  = {2021},

date = {2021-03-00},

journal = {Automatica},

volume = {125},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}