中国电机工程学报2025，Vol.45Issue(24)：9764-9775,中插24,13.DOI:10.13334/j.0258-8013.pcsee.241387
基于多图谱融合分析的GIS设备机械振动缺陷负载电流自适应诊断方法
Load Current Adaptive Diagnosis Method for Mechanical Vibration Defects of GIS Equipment Based on Multi-graph Fusion Analysis
摘要
Abstract
The mechanical vibration defect signals of gas insulated switchgear(GIS)equipment are highly complex.Addressing the prominent issue of limited feature extraction and low identification accuracy of mechanical defects based on vibration signal analysis due to dynamic changes in load current,this paper firstly uses the Markov transition field,spectral Markov transition field,and short-time Fourier transform method to transform the one-dimensional time series vibration signals into time domain,frequency domain,and time-frequency domain vibration graphs in turn,and the feature information of these three vibration graphs were fused using the multi-graph fusion method,which reduces the complexity of data processing effectively;then,the GoogLeNet is improved by using the adaptive batch normalization algorithm to construct the load current adaptive diagnosis model for GIS mechanical vibration defects with the fusion graphs as input.The results show that the improved model has a high diagnostic accuracy for typical mechanical vibration defects under variable load currents,with an average diagnostic accuracy of 91.21%,which is 28.14%higher than that of the unimproved GoogLeNet model,providing a valuable reference for the diagnosis of GIS equipment mechanical vibration defects under the dynamic change of load currents in the field.关键词
气体绝缘组合电器/缺陷诊断/图谱融合/负载电流自适应/GoogLeNet模型Key words
gas insulated switchgear(GIS)/defect diagnosis/multi-graph fusion/load current adaptive/GoogLeNet model分类
信息技术与安全科学引用本文复制引用
郝建,李旭,邵子琦,廖瑞金,刘丛,宫瑞磊..基于多图谱融合分析的GIS设备机械振动缺陷负载电流自适应诊断方法[J].中国电机工程学报,2025,45(24):9764-9775,中插24,13.基金项目
国家重点研发计划项目(2022YFB2403700,2022YFB2403705) （2022YFB2403700,2022YFB2403705）
重庆市自然科学基金项目(CSTB2022NSCQ-MSX1247).National Key R&D Program of China(2022YFB2403700,2022YFB2403705) （CSTB2022NSCQ-MSX1247）
Natural Science Foundation of Chongqing(CSTB2022NSCQ-MSX1247). （CSTB2022NSCQ-MSX1247）
