TY - GEN
T1 - Condition-Based Maintenance Program on Lithium-Ion Batteries Using Artificial Intelligence for Aeronautical Operations Management
AU - Garay, Fernando
AU - Huaman, William
AU - Atoche, Wilmer
AU - Franco, Elmar
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Aircraft maintenance
KW - Artificial intelligence
KW - Gaussian process
KW - Lithium-ion battery
UR - http://www.scopus.com/inward/record.url?scp=85140711352&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-06862-1_10
DO - 10.1007/978-3-031-06862-1_10
M3 - Conference contribution
AN - SCOPUS:85140711352
SN - 9783031068614
T3 - Springer Proceedings in Mathematics and Statistics
SP - 137
EP - 151
BT - Production and Operations Management - POMS 2021
A2 - Vargas Florez, Jorge
A2 - de Brito Junior, Irineu
A2 - Leiras, Adriana
A2 - Paz Collado, Sandro Alberto
A2 - González Alvarez, Miguel Domingo
A2 - González-Calderón, Carlos Alberto
A2 - Villa Betancur, Sebastian
A2 - Rodríguez, Michelle
A2 - Ramirez-Rios, Diana
PB - Springer
T2 - International Conference on Production and Operations Management, POMS 2021
Y2 - 2 December 2021 through 4 December 2021
ER -