Multisource Deep Adversarial Decoupled Autoencoder Network for State Recognition of High-Speed Train Brake Pads

High-speed train brake pads state recognition faces the problems of single data source feature characterization limitation and significant domain shifts under variable working conditions. Considering that multi-source heterogeneous data can characterize the brake pad state from different physical dimensions, this paper proposes a multi-source deep adversarial decoupled autoencoder network (MS-DADA) for online identification of brake pad state of high-speed trains under variable working conditions. First, a signal characterization system covering the multidimensional state characteristics of the friction interface is constructed by fusing three kinds of multi-source heterogeneous data, including friction coefficient, tangential acceleration and noise. Second, a deep adversarial decoupled autoencoder is designed to realize the explicit decoupling of domain-invariant and domain-specific features by utilizing the synergistic mechanism of mutual information minimization constraint and domain adversarial. Finally, with the validation set accuracy as the optimization objective, a genetic algorithm is introduced to dynamically allocate multi-source weights. This adaptive weighted fusion strategy significantly enhances the model's generalization capability for unknown rotational speed conditions. The experimental results of 10 cross-speed tasks show that the proposed model achieves an average accuracy of 99.12%. It is 7.1%, 9.36% and 26.5% higher than the single-source model, and 3.58% to 6.36% better than the current leading domain generalization methods.