MBQG-Net: A Multi-Scale Bidirectional Query-Guided Gated Network for Aero-Engine Remaining Useful Life Prediction

Remaining Useful Life (RUL) prediction for aircraft engines is a critical task in prognostics and health management, aiming to extract degradation information from multi-sensor operational data to support maintenance scheduling and operational risk management. Existing hybrid temporal models have explored various combinations of convolutional networks, temporal convolutions, recurrent networks, and attention mechanisms; however, there remains room for further coordination regarding the positional roles of different modules within fixed windows, bidirectional context organization, and training objectives. To address this, we propose a Multi-scale Bidirectional Query-Guided Gated Network, MBQG-Net. MBQG-Net employs one-dimensional convolution and parallel bidirectional dilated convolutions to extract local and multi-range temporal features, and utilizes multi-head Query–Value temporal attention to generate window-level context-enhanced representations prior to recurrent state aggregation. The sequence enhanced by Query-based temporal weighting and Value aggregation is then fed into a stacked bidirectional GRU for subsequent bidirectional gated state aggregation. During training, an asymmetric weighted mean squared error is adopted, assigning higher weights to RUL overestimation errors. MBQG-Net was evaluated on all four official test sets (FD001–FD004) of the NASA C-MAPSS dataset under a unified experimental protocol. It achieved the strongest overall regression performance on FD001 and FD003 and maintained competitive overall conventional regression performance on the multi-condition FD002 and FD004 subsets. The model obtained the lowest NASA Score among the compared methods on all four subsets, although the difference on FD002 was marginal. On FD004, MBQG-Net achieved a NASA Score of 930.9088, representing a 28.27% reduction relative to the second-best result. Experimental results demonstrate that MBQG-Net maintains a favorable balance between conventional regression accuracy and direction-sensitive error control across different operating-condition and fault-mode settings, offering a structurally clear hybrid temporal modeling solution for multi-sensor aircraft engine RUL prediction.