A Self-Attention Integrated Learning Model for Landing Gear Performance Prediction

Abstract

The landing gear structure suffers from large loads during aircraft takeoff and landing, and an accurate prediction of landing gear performance is beneficial to ensure flight safety. Nevertheless, the landing gear performance prediction method based on machine learning has a strong reliance on the dataset, in which the feature dimension and data distribution will have a great impact on the prediction accuracy. To address these issues, a novel MCA-MLPSA is developed. First, an MCA (multiple correlation analysis) method is proposed to select key features. Second, a heterogeneous multilearner integration framework is proposed, which makes use of different base learners. Third, an MLPSA (multilayer perceptron with self-attention) model is proposed to adaptively capture the data distribution and adjust the weights of each base learner. Finally, the excellent prediction performance of the proposed MCA-MLPSA is validated by a series of experiments on the landing gear data.

The Optimization of a Model for Predicting the Remaining Useful Life and Fault Diagnosis of Landing Gear

“…Lin et al focused on predictive maintenance of aircraft landing gear using a selfattention integrated learning model [6] and proposed multiple correlation analysis and a multilayer perception with the self-attention (MLPSA) method. First, three coefficients, including the Pearson coefficient, Spearman coefficient, and Kendall coefficient, were integrated to establish indicators that were used to select key features.…”