Probabilistic damage tolerance analysis using inspection data from integrated sensors

ABSTRACT : Fatigue failures are common failures within the aeronautical field. microcracks appear after many repetitions of cyclic stresses, then, these microcracks grow until a point of no return is reached and the growth becomes unstable and imminent. It is hard to do reliable estimations of a sys...

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Autores:
Vallejo Ciro, María Isabel
Carvajal Loaiza, Manuel José
Tipo de recurso:
Trabajo de grado de pregrado
Fecha de publicación:
2021
Institución:
Universidad de Antioquia
Repositorio:
Repositorio UdeA
Idioma:
eng
OAI Identifier:
oai:bibliotecadigital.udea.edu.co:10495/25415
Acceso en línea:
http://hdl.handle.net/10495/25415
Palabra clave:
Aeronautics - Safety measures
Aeronáutica - medidas de seguridad
Fracture of solids
Fractura de sólidos
Fracture strength
Resistencia a las fracturas
Materials fatigue
Fatiga de materiales
Probabilities
Probabilidades
Residual stresse
Esfuerzos residuales
Stability of airplanes
Estabilidad de los aviones
Sensors
Sensores
Bayesian updating
http://aims.fao.org/aos/agrovoc/c_28279
Rights
openAccess
License
http://creativecommons.org/licenses/by-nc-sa/2.5/co/
Description
Summary:ABSTRACT : Fatigue failures are common failures within the aeronautical field. microcracks appear after many repetitions of cyclic stresses, then, these microcracks grow until a point of no return is reached and the growth becomes unstable and imminent. It is hard to do reliable estimations of a system subject to fatigue due to multiple random factors that affect the material, geometry, and stresses, among others. In this work, to estimate this type of failure, a probabilistic analysis is performed, where each parameter from the model is represented as a probability density function. This work presents a software application to perform fatigue failure analysis using MATLAB and SMART|DT. This last program follows the standards issued by the Federal Aviation Administration of United States (FAA). The application implements a Bayesian inference process to update the model’s crack size distribution when inspections are performed, to add more accuracy to risk predictions. This application is expected to support decisions about when to perform inspections based on an allowable desired risk for the fleet.