Guadiana Alvarez, José Luis | Tecnológico De Monterrey |
Montoya Herrera, Luisa Fernanda | Tecnológico De Monterrey |
Vargas-Martínez, Adriana | Tecnológico De Monterrey |
Lozoya-Santos, Jorge de-J | Tecnológico De Monterrey |
Ramírez-Mendoza, Ricardo A. | Tecnológico De Monterrey |
Morales-Menendez, Ruben | Tecnológico De Monterrey |
Resumen: Condition-based maintenance (CBM), is a maintenance strategy that is based on the continuous monitoring of systems. With Fault Prognosis, prediction such as at what time an element of a system will fail, or its Remaining Useful Life can be made. A review on Fault Prognosis and its most used algorithms to apply it, evaluating the obtained results is made. While Machine Learning is the preferred methodology, a hybrid algorithm using data-driven and knowledge-based methods can sometimes offer a better solution, depending on the system and the available data from it.
¿Cómo citar?
Jose L. Guadiana Alvarez, Luisa F. Montoya Herrera, Adriana Vargas Martinez, Jorge de J. Lozoya Santos, Ricardo A. Ramirez Mendoza & Ruben Morales Menendez. Data-Driven Based Fault Prognosis for Systems – a Review. Memorias del Congreso Nacional de Control Automático, pp. 483-488, 2019.
Palabras clave
Detección y Aislamiento de Fallas, Redes Neuronales
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