@ARTICLE{Pra_App_2021,
author = "Prada Botia, Gaudy Carolina - PABON LEON, JHON ANTUNY - Orjuela Abril, Martha Sofia ",
title = "Application of neural and bayesian networks in diesel engines under the flaw detection method",
abstract = "The identification of premature faults in Internal Combustion Engines has become
determinant to guarantee suitable operation. Therefore, this study focuses on the implementation
of fault diagnostic methodology by using advanced algorithms such as Back Propagation neural
networks and Bayesian networks. Results indicated that the proposed methodology serves as a
robust tool to identify different fault conditions in a wide operational spectrum with an reliability
of nearly 73%. Moreover, the Backpropagation network diagnostic methodology presented an
reliability of 18%, which is 3% higher than Bayesian networks. Overall, the implemented
methodology counterbalanced interference conditions and noise signals while providing
versatility to operate for different types of engines. In conclusion, this study can be extrapolated
to different fields of physics to assist in identifying flaws in experimental test benches.",
year = 2021,
url = "https://repositorio.ufps.edu.co/handle/ufps/6620",
}