一种用于机器声音异常检测的ARViTrans方法

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doi：10.19734/j.issn.1001-3695.2024.10.0365

ARViTrans method for machine sound anomaly detection

ChenLonga，b，Guo Fabina，bt，Huang Xiaoweia，b，Lu Yashia，b （a.Schoolofifcaellgee&igtdslqpntStatesEaoFultredico&tee University，Hefei ，China）

Abstract：Inordertosolvetheproblems thattheexisting machine soundanomalydetection methodsonly focus onthe single featuresof the time，frequencyorchanneldimensions，ignoringthemutualconnectionbetweenthespectralfeaturesandthe timeseriesinformation，andtheinitial featurelossleads toinaccurate fitingof thesampledatadistribution，thuscausing a highanomaly mised detectionrateandfalse alarmrate，this paper proposedARViTrans，amachine sound anomalydetection methodthatintegratedatentionmechanismsandskipconnections.Firstlythispaperproposedathree-dimensionalicient coordinateatentionmechanismstocollaborativelycapturethetimedomain，frequencydomainandchanneldimensionfeatures through thedecouplingoperationofthefeaturespace.Secondly，itusedMobileViTasthebackbonenetworkanddesignedthe RES-MoViT module toreplacethe original MobileViT module.Skipconnections captured the information between the input andoutputand beterfitthesampledata distribution.Thegradientrefluxreduced therepeatedlearningof similarfeatureparametersandimprovedtheparameterutilizationeficiency.Finally，itcomparedtheexperimentalresultsontheMMdataset with the AE and MobileNetV2 of the DCASE Task2 baseline system. The AUC improves by 10.14% and 10.26% ，respectively.The pAUC improves by 13.40% and 6.50% ，respectively. The experimental results indicate that the proposed method caneffectivelycapturethemutualconnectionbetween featuresofdiferent dimensions while maintainingalowmodelcomplexity，improve the accuracy of anomaly detection and reduce the false alarm rate.

Key words：anomaly detection；MobileViT；attention mechanism；residual connection；unsupervise

0 引言

近年来，工业机器设备的状态监测在工厂自动化领域中发挥着至关重要的作用[1]。（剩余21819字）