Maintenance Service Events Prediction Modeling of Aircraft Gas Turbine Engines
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Abstract
This work addresses the PHM North America 2025 Conference data challenge for multi-event Remaining Useful Life (RUL) estimation on aircraft gas turbine engine modules, predicting the time-to-event for three maintenance actions: High Pressure Turbine shop visits (HPT SV), High Pressure Compressor shop visits (HPC SV), and Water Wash (WW).
We present a comprehensive workflow that integrates snapshot data quality control, virtual sensing for missing sensors (P25 and T5, and event-specific modeling with consensus mechanisms. Long short-term memory (LSTM) regression models are trained for HPC and WW using a custom loss function adapted from the competition, which heavily penalizes errors on early and near-term events. HPT RUL is produced by a confluence of an Artificial Neural Network (ANN) regressor and a linear degradation prior to stabilize extrapolation. A profile registration algorithm reconstructs temporal ordering in shuffled test/validation files, preserving health indicator (HI) monotonicity and degradation physics, proving a vital sanity check and building trust on the submitted results.
The MathWorks team achieved 1st place in the public test phase with the best submission score of 0.3528, proving the high quality of predictions. The functionalities and tools demonstrated in our work are generally applicable aircraft fleet maintenance services RUL predictions.
How to Cite
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PHM, Prognostics, Health Management, Health Indicator Design, Remaining Useful Life, RUL, Aircraft Engine
Moradi, Morteza, et al. “Intelligent Health Indicator Construction for Prognostics of Composite Structures Utilizing a Semi-Supervised Deep Neural Network and SHM Data.” Engineering Applications of Artificial Intelligence, vol. 117, Jan. 2023, p. 105502. DOI.org (Crossref), https://doi.org/10.1016/j.engappai.2022.105502.
Zou, Hui, and Trevor Hastie. “Regularization and Variable Selection Via the Elastic Net.” Journal of the Royal Statistical Society Series B: Statistical Methodology, vol. 67, no. 2, Apr. 2005, pp. 301–20.
Saxena, Abhinav, Kai Goebel, Don Simon, Neil Eklund. “ Damage propagation modeling for aircraft engine run-to-failure simulation." In Prognostics and Health Management, 2008. PHM 2008. International Conference on, pp. 1-9. IEEE, 2008.
Hochreiter, S., and J. Schmidhuber. "Long short-term memory." Neural computation. Vol. 9, Number 8, 1997, pp.1735–1780.
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