Ensemble Learning Based Convolutional Neural Networks for Remaining Useful Life Prediction of Aircraft Engines



Published Oct 26, 2023
Thambirajah Ravichandran Bolun Cui Sri Namachchivaya Amar Kumar Alka Srivatsava Yuan Liu


Remaining useful life (RUL) prediction is an essential task of Prognostics and Health Management (PHM) of aircraft engines performed utilizing the data collected from multiple sensors to ensure their safety. While many studies have been reported on RUL prediction for aircraft engines, only a few of them focus on ensemble learning based convolution neural network (CNN) models for RUL prediction. This paper proposes a new data-driven approach based on a multistage ensemble learning strategy for developing CNN models for RUL prediction of aircraft engines. The proposed approach places a major emphasis on generating diverse CNN models by exploring 2D CNN models and 1D CNN models with multiple channels and developing a multistage ensemble approach employing sparsity promoting model selection and weight learning methods to utilize only a subset of available models thus improving the RUL prediction performance. The effectiveness of the proposed approach is validated using the NASA C-MAPSS dataset for aircraft engines.

How to Cite

Ravichandran, T., Cui, B., Namachchivaya, S., Kumar, A., Srivatsava, A., & Liu, Y. (2023). Ensemble Learning Based Convolutional Neural Networks for Remaining Useful Life Prediction of Aircraft Engines. Annual Conference of the PHM Society, 15(1). https://doi.org/10.36001/phmconf.2023.v15i1.3517
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Remaining useful life, ensemble learning, convolutinal neural networks, hyperparameter optimization

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