基于改進(jìn)多尺度卷積網(wǎng)絡(luò)的軸承故障診斷研究

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中圖分類號(hào)：TH133.3 文獻(xiàn)標(biāo)識(shí)碼：A 文章編號(hào)：2096-4706（2025）07-0179-07

Abstract： In this paper，Improved Multi-Scale Convolutional Networksbearing fault diagnosis method is proposed to solvethe problemsofConvolutionalNeuralNtworkincomplexevironments，suchassytobedisturbed，difculttoetract rich fault featuresfromfixedreceptivefeldandlowdiagnosisaccuracy.Firstly，theoriginalvibrationsignal ispreproced. Secondly，theconvolutionkerelsofdifrentreceptivefieldsareusedtoextractmulti-salefeaturestoeffectivelyapture diversifedfaultinformation.TrdlytheSelf-AentionMechanismisintroducedtoenablethemodeltodynamicallalculate andadjustthe weight ofeach position inthe feature map，and adaptivelyenhance the keyfault features.Finaly，the fully conected layer isused toclasifytheextracted features toachieveaccurate diagnosis.Theexperimentalresultsshowthatthe diagnosis accuracy of the method on the public dataset reaches about 98 % ，and it shows good anti-noise and generalization ability underdifferent signal-to-noise ratio conditions.

Keywords：Multi-ScaleConvolutionalNetworks;featureextraction;Self-AtentionMechanism;bearing fault diagnosis

0 引言

隨著裝備制造業(yè)的發(fā)展，軸承性能直接影響設(shè)備表現(xiàn)[1]。（剩余10638字）