A Bayesian CNN-LSTM Framework with Novel Dual Uncertainty Calibration for Aircraft Engine RUL Prognostics

The aircraft engine is a core component of the airplane, and its operational condition is directly related to the safety of both the equipment and personnel. Accurate Remaining Useful Life (RUL) prediction can help identify potential failure risks in advance, prevent unexpected breakdowns, and ensure operational safety throughout the flight. This paper presents a novel deep learning framework for predicting the RUL of aircraft engines, integrating Bayesian inference with Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and an attention mechanism. The proposed method first utilizes an attention mechanism to weigh the importance of different sensor inputs, followed by CNNs to extract salient spatial features from these readings, which are then fed into LSTMs to model the temporal dependencies inherent in engine degradation trajectories. Uncertainty quantification is a central focus, employing Bayesian techniques and Monte Carlo sampling at inference to distinguish and estimate both epistemic (model) and aleatoric (data) uncertainty. A key contribution is an innovative dual uncertainty calibration method that optimizes calibration parameters for both uncertainty types via maximum likelihood estimation, significantly improving the reliability of uncertainty estimates. Experiments conducted on the C-MAPSS dataset demonstrate the effectiveness of the approach. The model achieves optimal prediction accuracy with a specific input sequence length, and the dual calibration technique reduces calibration error substantially, leading to well-calibrated confidence intervals with coverage rates close to nominal levels. This methodology offers enhanced RUL prediction accuracy and robust uncertainty quantification, providing valuable support for predictive maintenance decisions.