Complete Life Cycle Degradation Trajectory Learning Utilizing Time-Transformer and Denoise Auto-Encoder for Remaining Useful Life Prediction

Deep Learning (DL) has substantially expanded its role in Prognostics and Health Management (PHM), particularly by enabling automated feature extraction for Remaining Useful Life (RUL) prediction. Despite this progress, existing DL models such as Long Short-Term Memory (LSTM) networks still face challenges in accurately capturing complete life-cycle degradation trajectories. To address this limitation, this study introduces a hybrid semi-supervised model that integrates a Time-Transformer (TT) with a Denoising Autoencoder (DAE), termed TT-DAE. The DAE first extracts spatial features and suppresses noise through signal reconstruction from degraded inputs. These extracted features are then separated into source and target domains and normalized to a uniform sequence length using a padding strategy. Subsequently, the TT module leverages both source features and a Sliding Variable-Length Window (SVW) mechanism to learn full degradation trajectories. A comprehensive experimental evaluation conducted on the C-MAPSS dataset demonstrates the effectiveness of the proposed approach, achieving an average Pearson Correlation Coefficient (PCC) of 0.89 between the predicted and actual target signals.