Interpretable and efficient RUL prediction of turbofan engines using EM-enhanced Bi-LSTM with TCN and attention mechanism

Author:

Wang JianxingORCID,Wang Yue,Li Jian

Abstract

Abstract Remaining useful life (RUL) prediction for turbofan engines is important in prognostics and health management (PHM) for the maintenance and operation of critical equipment. With continuous innovations in deep learning techniques, the complexity of models continues to increase, but the interpretability and comprehensibility of the prediction results become particularly important in industrial applications. Therefore, in this study, an improved bidirectional long and short-term memory network (Bi-LSTM) based interpretable hybrid deep learning model for RUL prediction of turbofan engines is proposed, which ingeniously integrates time series convolutional networks (TCNs), expectation maximization (EM), Bi-LSTMs, and attention mechanisms. By capturing time-series features at different levels, the model adapts to the complex dynamics of turbofan engine performance evolution in an efficient and cost-effective manner. Experimental validation on the C-MAPSS dataset demonstrated that the model significantly outperforms other methods in terms of RUL prediction performance, especially in improving prediction accuracy and coping with the degradation of complex system dynamics. The largest contribution of key metrics to the model is validated through consistent results from multiple interpretable tools, providing comprehensive and consistent support for understanding and trusting prediction results in industrial applications. This study further enhances the robustness of the model and the reliability of the interpretable results by delving into the dynamic relationships between the properties of the different life stages, which not only reveal the importance of these characteristics in engine life prediction but also provide more comprehensive information about the engine performance variations by observing the dynamic relationships.

Publisher

IOP Publishing

Reference40 articles.

1. Machine learning based approach for forecasting emission parameters of mixed flow turbofan engine at high power modes;Aygun;Energy,2023

2. Self-attention transformer-based architecture for remaining useful life estimation of complex machines;Wahid;Procedia Computer Science,2023

3. ‘Aero engine health monitoring, diagnostics and prognostics for condition-based maintenance: an overview;Narahari;International Journal of Turbo & Jet-Engines,2024

4. Multi-task spatio-temporal augmented net for industry equipment remaining useful life prediction;Li;Adv. Eng. Inf.,2023

5. Remaining useful life prediction of lithium-ion batteries using EM-PF-SSA-SVR with gamma stochastic process;Keshun;Meas. Sci. Technol.,2023

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