Condition-Based Maintenance Program on Lithium-Ion Batteries Using Artificial Intelligence for Aeronautical Operations Management

Fernando Garay, William Huaman, Wilmer Atoche, Elmar Franco

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva


On 2013, all Boeing 787 were grounded due to events of deflagration in lithium-batteries installed in these aircraft, it generated subsequently changes in the flight itinerary, dissatisfaction in customers and expenses in maintenance costs in many companies around the world, losing about $22,000 per hour. For this reason, condition-based maintenance program was performed using State of Health and Remaining Useful Life indicator. A new technique Machine Learning was used for solves regression problems in non-parametric data, called Gaussian Processes, this emerging algorithm of Artificial Intelligence generates predictive models based on previous knowledge, giving a probability distribution that follows the current state, allowing interpret the reliability of the component in different cycles of useful life. The paper used the dataset from the NASA repository, due to it has the same internal composition and is tested run to failure. Kernel mixed Matern1.5 + Matern2.5 got good results versus other mixtures during the different test, mapping the real behavior of the battery. The health status diagnostic was quantitatively evaluated and it got results of 98.34% and 1.13% in R2 and in RMSE respectively, likewise the model served to forecast the remaining useful life of the battery, predicting 64 cycles with a minimum error of 1.53% in reference to the real data. Finally, it helped development a condition-based predictive maintenance program that generated a return on investment (ROI) of 173% and a profit of $331,360 during the first year.

Idioma originalInglés
Título de la publicación alojadaProduction and Operations Management - POMS 2021
EditoresJorge Vargas Florez, Irineu de Brito Junior, Adriana Leiras, Sandro Alberto Paz Collado, Miguel Domingo González Alvarez, Carlos Alberto González-Calderón, Sebastian Villa Betancur, Michelle Rodríguez, Diana Ramirez-Rios
Número de páginas15
ISBN (versión impresa)9783031068614
EstadoPublicada - 2022
Publicado de forma externa
EventoInternational Conference on Production and Operations Management, POMS 2021 - Virtual, Online
Duración: 2 dic. 20214 dic. 2021

Serie de la publicación

NombreSpringer Proceedings in Mathematics and Statistics
ISSN (versión impresa)2194-1009
ISSN (versión digital)2194-1017


ConferenciaInternational Conference on Production and Operations Management, POMS 2021
CiudadVirtual, Online


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