基于动态自适应的电梯钢带典型故障检测算法

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DOI：10.16652/j.issn.1004-373x.2025.16.026

中图分类号：TN919-34；TP391 文献标识码：A 文章编号：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

随着社会经济水平和城市化进程的迅速发展，电梯已成为人们必不可少的垂直交通工具，同时人们对于电梯的安全性和舒适性的要求日益增加，因此新材料、新工艺、新技术也逐渐在电梯制造、设计、运维中开展应用。（剩余6941字）