Natual gas pipeline fault intelligent diagnosis based on the Bayesian single-source domain generalization algorithm
Deep learning-based fault intelligent diagnosis models have been widely used in the research on gas pipeline transportation safety.However,gas pipelines usually operate in a quasi-steady state,resulting in a limited number of fault samples in the training set,which may impede the accuracy of fault diagnosis.In this regard,this paper presents a new fault diagnosis method based on Bayesian single-domain generalization(BSDG).The core of the BSDG algorithm lies in deploying an attack-defense strategy that enhances the model's adaptability and robustness under different domain perturbation settings by specifying the pseudo-target domain augmentation paths in the attack phase and adjusting the posterior distributions of the model's parameters in the direction of the higher pseudo-domain samples scores in the defense phase.The results show that the Bayesian network-based untargeted attack model ensures that the pseudo-domain samples retain correlation with the source domains while introducing enough domain discrepancies to simulate the potential target domains,which aims to improve the diagnostic accuracy in both the multi-source and single-source domain settings.The testing results indicate that the BSDG algorithm improves the performance over the best contrast algorithm(SSAA algorithm)by 9.79％,5.09％,and 27.98％in the multi-source domain generalization task and the two single-source domain generalization tasks,respectively.The margin discrepancy loss allows the classifier to be flexible and effective in dealing with frequent distribution variations by introducing uncertainty in the process of learning the decision boundaries.The significance tests show that the BSDG algorithm significantly outperforms the state-of-the-art contrast algorithms in most scenarios.The Bayesian neural network effectively improves the generalization stability of the BSDG algorithm by introducing uncertainty on the weight.It is concluded that the BSDG algorithm effectively extends the decision boundary of the source-domain model and improves the diagnostic generalization performance by using the attack-defense strategy based on the Bayesian inference algorithm,which provides theoretical support for the design of fault diagnosis models of gas pipelines with limited samples.
万方数据
维普
