基于多分支CNN與改進(jìn)級聯(lián)森林的故障診斷

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中圖分類號：TP391 文獻(xiàn)標(biāo)志碼：A

Abstract：Infaultdiagnosis，deeplearningmodelssuchasConvolutionalNeuralNetworks（CNN）andDepForesthavedemonstrated outstandingperformance，atractingsignificantaention.However，single-branchCNsextractliitedfaultfeatures，ndthemultigrainedscainginDeepForestrequiresredesigningandajustingparametersfordiferentdatasets.Thispaperproposesahbriddep learningmodelthatcombinesamulti-branchCNNwithanimprovedcascadeforest.Firstly，amulti-branchCNwithdiferentcnvolutionalkerelsisigdtoactveeauresiaalllisfopes.codlyincEtG dient Boosting（XGBost）handlesnonlineardatabeterthanrandomforest，onerandomforestinthecascadeforest isreplacedwith XGBoost.Thispartialeplacementleveragestheadvantagesofdiferentalgoris，tiizigtheodel'soverallpeformaneFinally，ahybriddplearning modelcombines themulti-branch CNNandtheimprovedCascadeForest.Experimentsconductedonthree bearingdatasets and one rotor dataset demonstrate the proposed model's strong effctiveness in fault diagnosis.

Keywords：faultdiagnosis;CNN;cascade forest;XGBoost

對軸承和齒輪準(zhǔn)確的故障診斷是保證設(shè)備安全運行的基礎(chǔ)，對工業(yè)領(lǐng)域的持續(xù)發(fā)展至關(guān)重要[1-3]

隨著技術(shù)的發(fā)展，利用計算機(jī)和人工智能診斷機(jī)械故障成為一個熱門話題[4]。（剩余13882字）