基于動(dòng)態(tài)自適應(yīng)的電梯鋼帶典型故障檢測(cè)算法

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DOI:10.16652/j.issn.1004-373x.2025.16.026
中圖分類(lèi)號(hào):TN919-34;TP391 文獻(xiàn)標(biāo)識(shí)碼:A 文章編號(hào):1004-373X(2025)16-0161-06
Dynamicself-adaptiveelevatorsteelbelttypical faultdetectionalgorithm
ZHOUZecheng1,LIChen1,XUFeng2,ZHANGCai3,HUANGKanfei2 (1.CollegeofEnergy,Environmentand Safety Engineering,ChinaJiliangUniversity,Hangzhou 31o8,China; 2.ZhejiangAcademyofSpecialEquipment Science,Hangzhou 31ooo9,China; 3.Wenzhou Special Equipment Inspection& Science Research Institute,Wenzhou 325oo1,China)
Abstract:Inalusion totheproblems ofpoorreal-timedetection,lowaccuracyandcomplex processintraditional elevator stlbeltypicalfaults,nlevatorstelbelttypcalfaultetetionalgoribsedonamicself-adapation,DOis proposed.ThedeformableconvolutionDCNv2isusedtoreplacetraditionalconvolutionlayers,soastobeteradapttovariations inshapesand structuresoffaults.Themulti-scalelarge kernel separable (MLKS)moduleisconstructedtoenhancethe model's adaptabilitytodiferentfeaturescalesandspatialvariations.Adual-pathchannelatention(DPCA)mechanismisproposedto strengthenthemodel'scapabilityinfeatureperception,extraction,andfusiononthechaneldimension.Adynamicshared alignment(DSA)detection headisdesigned tooptimizethe independence,irelevance,and featureconflictisuesduring localizationandclassificationtasks.Theexperimentalresultsshowthat,ontheelevatorsteelbelttypicalfaultdatasets,DSMAYOLO can increase mAP@0.5and mAP@0.5~0.95by4.4%and10.1%respectively compared with the baselinealgorithm YOLOv8n,whichisbeterthanothercomparativeobjectdetectionalgorithm,andcanmet thereal-timerequirementsfor elevatorsteel belt typical fault detection,providing references for elevator fault diagnosis methods and applications.
Keywords:elevatorsteelbelt;faultdetection;dynamicself-adaption;YOLOv8n;deformableconvolution;dual-chael attention mechanism; feature sharing
0 引言
隨著社會(huì)經(jīng)濟(jì)水平和城市化進(jìn)程的迅速發(fā)展,電梯已成為人們必不可少的垂直交通工具,同時(shí)人們對(duì)于電梯的安全性和舒適性的要求日益增加,因此新材料、新工藝、新技術(shù)也逐漸在電梯制造、設(shè)計(jì)、運(yùn)維中開(kāi)展應(yīng)用。(剩余6941字)