现代电子技术2025，Vol.48Issue(10)：52-56,5.DOI:10.16652/j.issn.1004-373x.2025.10.009
融合数字孪生与声纹识别的矿用机电设备故障诊断技术
Mine electromechanical equipment fault diagnosis technology integrating digital twin and voiceprint recognition
摘要
Abstract
In allusion to the problem that mining electromechanical equipment is prone to faults in complex working environments,the digital twin technology of computer mapping physical entities and fault voiceprint feature recognition technology are researched,and a multi-architecture intelligent fault diagnosis system for mining electromechanical equipment from bottom to application is proposed.In the overall architecture of the proposed fault diagnosis system,the digital twin that includes behavior models,geometric models,rule models and physical models,as well as sensors and edge data processing modules required for voiceprint recognition are constructed in the bottom player.The upper layer is divided into data layer,system layer and application layer,which are used to realize functions such as data processing,model analysis and human-machine interaction.Based on digital twin system,voiceprint feature extraction algorithm,and extreme learning machine neural network,a key algorithm flow integrating digital twin and voiceprint recognition is designed.The experimental testing results show that the physical parameter error of digital twin simulation is low,and the accuracy of fault identification can reach about 90%,which can meet the needs of engineering applications.关键词
矿用机电设备/数字孪生/声纹识别/故障检测/智能化诊断/特征提取Key words
mine electromechanical equipment/digital twin/voiceprint recognition/fault detection/intelligent diagnosis/feature recognition分类
电子信息工程引用本文复制引用
张荣华..融合数字孪生与声纹识别的矿用机电设备故障诊断技术[J].现代电子技术,2025,48(10):52-56,5.基金项目
国源电力科信2023年度科技项目及技术标准项目(GSKJ-23-65) （GSKJ-23-65）
