Vectorial importance-weighted neural network framework for aviation structural systems multi-failures related reliability estimation

To achieve multi-failure related reliability estimation of aviation structural systems, the vectorial importance-weighted neural network framework (VIWNF) is developed by fusing the matrix theory, self-attention mechanism, compact support region (CSR) thought, neural network model, multi-objective black-winged kite (MOBWK) algorithm, synchronous sampling mechanism, and Copula strategy. In this framework, the matrix theory is applied to convert known sample information and unknown parameters into vectors, matrix, and cells array; the self-attention mechanism is utilized to attribute various importance degrees for input variables; the CSR thought is adopted to obtain the weights of different samples; the neural network model is utilized to determine the correlation relationship; the MOBWK algorithm is tended to optimize the CSR; the synchronous sampling mechanism and Copula strategy are employed for multi-failure synchronous correlation reliability evaluation. In addition, the multi-objective mathematical benchmark case demonstrates the validity of the proposed VIWNF method from a mathematical viewpoint; the landing gear brake temperature (LGBT) and aeroengine turbine blade multi-failure are taken to validate the effectiveness of VIWNF approach in the engineering field. The results reveal that the explored method exhibits outstanding advantages in both modeling and simulation properties. The research work in this paper can provide guidance for aeroengine health monitoring and optimization design, and further enrich the multi-failure related reliability theory in aviation structural systems.