Landing gear condition monitoring based on back propagation neural network-based on multi-strategy cooperative optimization

Abstract

To effectively monitor the operation state of landing gear, a back propagation neural network-based on multi-strategy cooperative optimization(MSCO-BPNN) is proposed. The multi-strategy optimization algorithm is composed of chaotic mapping strategy, adaptive spiral capture strategy, crossover mutation strategy and whale optimization algorithm(WOA). WOA is applied to find the optimal hyperparameters of back propagation neural network(BPNN). The search efficiency, multi-local search ability and global search performance of model can be improved by using chaotic mapping strategy, adaptive spiral capture strategy and crossover mutation strategy. The BPNN with optimal hyperparameters is introduced to establish the implicit model of input parameters and output responses. Based on quick access recorder(QAR) data, landing gear left side brake temperature is act as the monitoring objective of this paper. The validity and applicability of MSCO-BPNN are verified by compared with WOA-BPNN, particle swarm optimization BPNN and traditional BPNN. The results show that MSCO-BPNN can monitor the operation status of landing gear with high efficiency and accuracy. The efforts of this paper provide a promising insight for the precise condition monitoring of complex structures.