An adaptive integrated learning-based virtual sensing framework for temperature prediction in aircraft brake monitoring engineering

In commercial aviation, effective deceleration relies on accurate brake temperature trends. Traditional thermocouple sensors exhibit discontinuous responses, while machine learning-based methods often struggle with abnormal fluctuations due to high-dimensional signal overlap during low-speed braking. This study proposes an Adaptive Thermal Damping Integrated (ATDI) virtual sensor based on integrated learning to enhance brake temperature monitoring accuracy and address these challenges. A novel feature selection method is proposed that includes extracting baseline variables from five brake-related physical models and identifying significant latent variables using Shapley values, which are calculated by integrating three correlation indices based on linear, nonlinear, and rank consistency differences from aircraft flight records. The ATDI framework employs a time-lagged loss function and self-attention mechanism for dynamic weight assignment in the feature space, capturing critical temporal correlations to determine the thermal trend. A digital approach, adaptive to aircraft kinetic energy and called virtual thermal damping, is incorporated at the end of the prediction to mitigate fluctuations and ensure compliance with thermal conduction principles in braking systems. Experimental results demonstrate that the ATDI framework outperforms comparison networks in temperature value continuity and anomaly fluctuation suppression, especially with Long Short-Term Memory (LSTM) as the meta-learner, across three evaluation metrics. Additionally, application strategies for the ATDI framework in aircraft operation and maintenance are proposed.