Evaluación del desempeño de tres algoritmos de inferencia bayesiana, implementados como sistema experto para la identificación de modos de falla en ejes
dc.rights.license | Atribución-NoComercial 4.0 Internacional |
dc.contributor.advisor | Espejo Mora, Edgar |
dc.contributor.author | Mappe Rojas, Kevin Adalberto |
dc.date.accessioned | 2020-02-19T18:59:06Z |
dc.date.available | 2020-02-19T18:59:06Z |
dc.date.issued | 2019-11-29 |
dc.identifier.uri | https://repositorio.unal.edu.co/handle/unal/75649 |
dc.description.abstract | El objetivo principal de este proyecto fue la evaluación del desempeño de la inferencia bayesiana implementada como un sistema experto para la identificación de los modos de fallo en ejes. El software experto se dividió en dos módulos, uno para modos de falla que involucran fractura y otro para modos de fallo que involucran deformación plástica, desgaste y corrosión. Se implementaron tres motores de inferencia bayesianos, dos para inferencia exacta y uno para inferencia aproximada que permitieron la identificación de modos de fallo a partir de una base de conocimiento y la evidencia ingresada al software mediante dos cuestionarios, uno para el módulo de deformación plástica, desgaste y corrosión y otro para el módulo de fractura. Para la base del conocimiento o probabilidades a prior se recopiló de un total de 280 casos de falla en ejes diagnosticados por expertos. Para cada modo de fallo identificado por el experto se realizó el análisis de las marcas características presentes en la zona de falla y con la cantidad de marcas encontradas por los expertos para cada modo de fallo, se conformó la base de datos y las redes bayesianas para el software experto. La evaluación y posterior comparación de los motores de inferencia bayesianos para el software experto consistió en un análisis cuantitativo de los resultados, que se obtuvo al evaluar un total de 62 casos de falla en ejes. Para este análisis se utilizaron las medidas de grupo, índice de acuerdo y kappa, usualmente utilizadas para la evaluación de software experto. Adicionalmente se utilizó la metodología de evaluación de ratios de acuerdo con sus índices (Sensibilidad, especificidad, ratio de falsos positivos, ratio de falsos negativos y ROC “Receiver operating characteristic”). Como resultado de la evaluación mediante los índices de acuerdo se determinó que para este desarrollo y específicamente para las redes bayesianas programadas para cada modo de fallo, el motor de inferencia que obtuvo mejores resultados fue Metropolis-hastings tanto para el módulo de fractura como para el módulo de deformación plástica, desgaste y corrosión. Finalmente, de este proyecto se obtiene un software experto implementado en el lenguaje de programación Python para la identificación de modos de fallo en ejes, con tres motores de inferencia bayesiano. |
dc.description.abstract | The main goal of this project is the evaluation of the Bayesian inference performance implemented as an expert software for failures modes identification in shafts. Expert software has two modules, one for failures modes with fracture and one for failure modes with plastic deformation, wear and corrosion. Three Bayesian inference engines were implemented, two for exact inference and one for approximate inference, the inference engine allow the identification of failure modes in shafts from a knowledge base and the evidence entered into the software through two questionnaires, one for the plastic deformation, wear and corrosion module and another for the fracture module. The prior probabilities or knowledge base were collected from a total of 280 case of failures in shafts diagnosed by experts. For each failure mode identified by expert, the analysis of the attributes present in the fault zone was performed. With the number of attributes found by experts for each failure mode, the Bayesian database and networks for expert software were formed. The comparison and evaluation of the Bayesian inference engine for the expert software consisted in a quantitative analysis for the result obtained by evaluating a total of 62 cases of shaft failure. For this analysis we used the group measures, agreement index and kappa, usually used for expert software evaluation. In addition, the methodology used to evaluate ratios according to their indices (Sensibility, specificity, false positive rate, false negative rate and ROC “Receiver operating characteristic”) was used. As a result of performance evaluation and according to the index of agreement, in this development and specifically for the Bayesian network programmed for each failure mode, showed that the best inference engine was Metropolis-hastings for fracture and for the module of plastic deformation, wear and corrosion. Finally, from this project we obtain an expert software implemented in the Python programming language for the identification of failure modes in shafts with three Bayesian inference engines. |
dc.format.extent | 149 |
dc.format.mimetype | application/pdf |
dc.language.iso | spa |
dc.relation | A performance evaluation of three inference engines as expert systems for failure mode identification in shafts |
dc.rights | Derechos reservados - Universidad Nacional de Colombia |
dc.rights.uri | http://creativecommons.org/licenses/by-nc/4.0/ |
dc.subject.ddc | Ingeniería y operaciones afines |
dc.subject.ddc | Tecnología (Ciencias aplicadas) |
dc.title | Evaluación del desempeño de tres algoritmos de inferencia bayesiana, implementados como sistema experto para la identificación de modos de falla en ejes |
dc.title.alternative | Performance evaluation of three Bayesian inference algorithms, implemented as an expert system for the identification of shaft failure modes. |
dc.type | Documento de trabajo |
dc.rights.spa | Acceso abierto |
dc.description.additional | Magíster en Ingeniería Mecánica. Línea de Investigación: Modos de falla en elementos de máquinas. |
dc.type.driver | info:eu-repo/semantics/workingPaper |
dc.type.version | info:eu-repo/semantics/acceptedVersion |
dc.contributor.researchgroup | Grupo de Investigación: AFIS (Análisis de Fallas, Integridad y Superficies). |
dc.description.degreelevel | Maestría |
dc.publisher.branch | Universidad Nacional de Colombia - Sede Bogotá |
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dc.rights.accessrights | info:eu-repo/semantics/openAccess |
dc.subject.proposal | Failure analysis |
dc.subject.proposal | Análisis de falla |
dc.subject.proposal | expert system |
dc.subject.proposal | Sistema experto |
dc.subject.proposal | inferencia bayesiana |
dc.subject.proposal | Bayesian inference |
dc.subject.proposal | redes bayesianas |
dc.subject.proposal | Bayesian network |
dc.subject.proposal | eliminación de variables |
dc.subject.proposal | Variable elimination |
dc.subject.proposal | Metrópolis-Hastings |
dc.subject.proposal | Metropolis-hasting |
dc.subject.proposal | enumeration |
dc.type.coar | http://purl.org/coar/resource_type/c_8042 |
dc.type.coarversion | http://purl.org/coar/version/c_ab4af688f83e57aa |
dc.type.content | Text |
dc.type.redcol | http://purl.org/redcol/resource_type/WP |
oaire.accessrights | http://purl.org/coar/access_right/c_abf2 |
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