融合数字孪生与声纹识别的矿用机电设备故障诊断技术

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中图分类号：TN911.7-34；TP391.9 文献标识码：A 文章编号：1004-373X（2025）10-0052-05

Abstract：In alusion totheproblem that mining electromechanical equipmentisprone to faultsincomplex working environments，thedigital twin technologyofcomputermappng physical entitiesandfaultvoiceprint featurerecognition technologyareresearched，ndamulti-architectureintellgentfaultdagnosissystemforminingelectromechanicalquipment frombotom toappicationisproposed.Intheoverallarchitectureoftheproposedfaultdiagnosissystem，thedigitaltwinthat includesbehavior models，geometric models，rulemodelsand physical models，aswellassensorsandedgedataprocessing modulesrequiredforvoiceprintrecognitionareconstructedinthebottomplayer.Theupperlayerisdividedintodatalayer， systemlayerandapplicationlayer，whichareused torealizefunctionssuchasdataprocesing，modelanalysisandhumanmachineinteraction.Basedondigitaltwinsystem，voiceprintfeatureextractionalgorithm，andextremelearning machineneural network，akeyalgorithmflowintegratingdigitaltwinandvoiceprintrecognitionisdesigned.Theexperimentaltestingresults showthatthephsicalparametererorofdigitaltwinsimulationislow，andtheacuracyoffaultidentificationcanreachabout 90% ，which can meet the needs of engineering applications.

Keywords：mineelectromechanicalequipment；digitaltwin；voiceprintrecognition;faultdetection;inteligentdiagnosis; featurerecognition

0 引言

目前，煤炭能源是我国最主要的能源类型之一，在煤炭开采过程中，矿用机电设备稳定运行是保证安全生产的前提。（剩余4902字）