Fault diagnosis of switch machine based on GAN-BO-BiGRU

NIU Hongxia; ZHU Xue

doi:10.11860/j.issn.1673-0291.20240022

Abstract：To address the widespread challenges of limited data samples and low diagnostic accuracy in existing switch machine fault diagnosis,this study takes the action power curve,a critical time-series signal of the S700K switch machine,as the research object and proposes a fault diagnosis model based on GAN-BO-BiGRU.First,a small number of power data samples collected by the Centralized Sig-nalling Monitoring system(CSM)are fed into a Generative Adversarial Network(GAN).Through ad-versarial training between the generator and the discriminator,more sample data are generated to ad-dress the issue of data scarcity.Second,a BO-BiGRU fault diagnosis algorithm model is established.The Bayesian Optimization(BO)algorithm is used to determine the optimal values of key hyperparam-eters for the Bidirectional Gated Recurrent Unit(BiGRU)model,including the number of hidden-layer neurons,the initial learning rate,and the L2 regularization parameter,thereby obtaining the opti-mal hyperparameter combination.By exploiting BiGRU's capability to capture information bidirection-ally,the proposed model more comprehensively mines patterns from the time-series power data of the switch machine.Finally,simulations are conducted using both the generated data and the original data as samples.The simulation results demonstrate that the data generated by GAN exhibits minimal dif-ference from the original data and can effectively serve as an augmented dataset for fault diagnosis.Moreover,compared to the Long Short-Term Memory(LSTM)model,the BO-BiGRU fault diagno-sis model improves the F1 score by 1.77%,indicating its superior ability to extract fault features and its effectiveness in enhancing the accuracy of switch machine fault diagnosis.