基于TimeGAN的軌道交通LTE-M故障預測研究

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中圖分類號：TP391 文獻標識碼：A 文章編號：2096-4706（2025）08-0010-06

Abstract：The Long TermEvolution of Metro （LTE-M） network fault prediction datasetof rail transit has the problems ofunbalancedsamplesandsmallamountofsampledatawhichimpacttheacuracyoffault prediction.Inordertosolvethe above problems，this paper proposes aresearch methodofLTE-Mfault predictionofrail transitbased onconditionalTime-series Generative Adversarial Networks （TimeGAN）.Bydefiningdynamic autoencoderandstatic autoencoderinTimeGANmodel，this method furtherexploresthedynamicandstaticcharacteristicsofLT-Mfaultdataofrailtransit，andintroducesGELUactivation functionnthepotentialspaceofgeneratoranddiscriminatortoaceleratemodelconvergenceandgeneratesyntheticdatacloser toreal data，thusefectivelyalleviating the problemofunbalancedfaultdatasetandsmalldatavoume.Theexperimentalresults showthatwhenthedatasynthesizedbytheTimeGANmodelisusedforfaultpredictiontraining，itcanproducebeterediction results than the original data.

Keywords：rail transitLTE-M;fault prediction;time-series;TimeGAN

0 引言

隨著新一代移動通信的飛速發(fā)展，軌道交通通信基礎設施規(guī)模也迅速擴展，LTE-M網絡作為軌道交通網絡關鍵組成部分，其復雜性也隨之增加。（剩余7847字）