🤖 AI Summary
This study addresses the challenge of insufficient accuracy and robustness in remaining useful life (RUL) prediction for aircraft engines under varying operating conditions and noisy measurements. To this end, the authors propose a bidirectional residual-corrected LSTM (Bi-cLSTM) model that integrates bidirectional temporal modeling with an adaptive residual correction mechanism. The approach further incorporates a condition-aware preprocessing pipeline—comprising operating-condition segmentation-based normalization, feature selection, and exponential smoothing—to significantly enhance generalization and noise resilience across complex, multi-regime scenarios. Experimental results on all four NASA C-MAPSS datasets demonstrate that the proposed method outperforms existing LSTM-based baselines, achieving state-of-the-art performance, with particularly pronounced gains in multi-condition settings.
📝 Abstract
Accurate Remaining Useful Life (RUL) prediction is a key requirement for effective Prognostics and Health Management (PHM) in safety-critical systems such as aero-engines. Existing deep learning approaches, particularly LSTM-based models, often struggle to generalize across varying operating conditions and are sensitive to noise in multivariate sensor data. To address these challenges, we propose a novel Bidirectional Residual Corrected LSTM (Bi-cLSTM) model for robust RUL estimation. The proposed architecture combines bidirectional temporal modeling with an adaptive residual correction mechanism to iteratively refine sequence representations. In addition, we introduce a condition-aware preprocessing pipeline incorporating regime-based normalization, feature selection, and exponential smoothing to improve robustness under complex operating environments. Extensive experiments on all four subsets of the NASA C-MAPSS dataset demonstrate that the proposed Bi-cLSTM consistently outperforms LSTM-based baselines and achieves competitive state-of-the-art performance, particularly in challenging multi-condition scenarios. These results highlight the effectiveness of combining bidirectional temporal learning with residual correction for reliable RUL prediction.