Lessons Learned from Aircraft Component Failure Prediction using Full Flight Sensor Data



Published Sep 4, 2023
Changzhou Wang Darren Puigh Audrey Lei Wei Guo Jun Yuan Mark Mazarek


Successful aircraft predictive maintenance relies on the accurate prediction of major aircraft component failures for operators to schedule and carry out maintenance operations before failure actually happens. In this paper, we share important lessons learned from our development of prognostics alerts using full flight sensor data, including various challenges of using big data, data quality issues, failure identification for data labeling, engineering-driven vs. data-driven methods, and aggregating alerts into actionable alerts. We also provide recommendations based on our experience with prognostic alerts developed and deployed for many airline operators.  

Abstract 333 | PDF Downloads 419



sensor data, predictive maintenance, data quality, machine learning, actionable alert

Darrah, T., Lovberg, A., Frank, J., Quinones-Gruiero, M., Biswas, G. (2022). Developing Deep Learning Models for System Remaining Useful Life Predictions: Application to Aircraft Engines. Annual Conference of the PHM Society, 2022.

Hodkiewicz, M., Ho, M. (2016). Cleaning historical maintenance work order data for reliability analysis. Journal of Quality in Maintenance Engineering. Volume 22, Issue 2, 2016.

Liaw, R., Liang, E., Nishihara, R., Moritz, P., Gonzalez, J., Stoica, I. (2018). Tune: A Research Platform for Distributed Model Selection and Training. July 13, 2018. https://arxiv.org/abs/1807.05118.

Lukens, S., Rousis, D., Thomas, D., Baer, T., Lujan, M., Smith, M. (2022). A Data Quality Scorecard for Assessing the Suitability of Asset Condition Data for Prognostics Modeling. Annual Conference of the PHM Society, 2022.

Mitici, M., de Pater, I., Barros, A., Zeng, Z. (2023). Dynamic predictive maintenance for multiple components using data-driven probabilistic RUL prognostics: The case of turbofan engines. Journal of Reliability Engineering and System Safety 234 (2023).

West, C. (2023). AI and the FCI: Can ChatGPT project an understanding of introductory physics? March 3, 2023. https://arxiv.org/pdf/2303.01067.pdf.

Yuan, J. (2022). Boeing Operationalized Aircraft Predictive Maintenance. AAAI Fall Symposia Series on Artificial Intelligence for Predictive Maintenance. Nov 19, 2022.
Special Session Papers