噪声与振动控制2025，Vol.45Issue(5)：123-130,8.DOI:10.3969/j.issn.1006-1355.2025.05.020
基于FRCMDE与IBOA-LSSVM的变压器故障声纹诊断方法
Fault Voiceprint Diagnosis Method for Transformers Based on FRCMDE and IBOA-LSSVM
摘要
Abstract
In order to improve the sensitivity of multiscale dispersion entropy to signal evolution and raise the accuracy of transformer fault voiceprint diagnosis,the fractional-refined composite multiscale dispersion entropy(FRCMDE)was ap-plied to transformer voiceprint feature extraction.Firstly,the parameters of FRCMDE are determined and the FRCMDE en-tropy features of transformer sound signals in different states were extracted.Secondly,the Improved Butterfly Optimization Algorithm(IBOA)was introduced to optimize the parameters of the Least Square Support Vector Machine(LSSVM)to con-struct an IBOA-LSSVM model,which was used to classify data features for transformer fault voiceprint diagnosis.Finally,to verify the effectiveness of this method,its result was compared with that of other classical methods.The research results show that the proposed FRCMDE-IBOA-LSSVM model can effectively distinguish transformer's sound signals in 8 states,and the diagnosis accuracy reaches 99.69%,which is higher than other methods.This method may provide a reference for transformer non-stop monitoring and fault voiceprint diagnosis.关键词
故障诊断/变压器/声纹诊断/分数阶精细复合多尺度散布熵/改进蝴蝶优化算法Key words
fault diagnosis/transformer/entropy characteristics of voiceprints/FRVMDE/IBOA分类
信息技术与安全科学引用本文复制引用
高家通,康兵,许志浩,王宗耀,丁贵立,袁小翠..基于FRCMDE与IBOA-LSSVM的变压器故障声纹诊断方法[J].噪声与振动控制,2025,45(5):123-130,8.基金项目
国家自然科学基金资助项目(62001202) （62001202）
