高压电器2025，Vol.61Issue(3)：205-213,9.DOI:10.13296/j.1001-1609.hva.2025.03.025
基于融合特征和残差神经网络的10 kV高压断路器机械故障声纹识别方法
Voiceprint Recognition Method for Mechanical Faults of 10 kV Circuit Breaker Based on Fusion Feature Residual Neural Network
摘要
Abstract
In view of such issues as relying too much on subjective experience,low accuracy and weak generaliza-tion ability of fault diagnosis method of traditional 10 kV high-voltage circuit breaker,a kind of common mechani-cal fault identification method for 10 kV high-voltage circuit breaker based on acoustic features is proposed.Firstly,type(ZN63)12/630A high-voltage indoor vacuum circuit breaker is taken as the research object,8 kinds of common mechanical faults are set and the sound during opening and closing operation is collected as the detection signal.Then,the collected fault voiceprint signals is preprocessed,the Mel cepstrum coefficient(MFCC)features,chromat features,one-dimensional average energy,and spectral centroid of the fault voiceprint signals are extracted,and Fisher ratio to discard components with lower contribution rates is used to form fusion features.Finally,the extract-ed fusion features is taken as the diagnostic basis,a residual neural network based mechanical fault diagnosis model for 10 kV circuit breaker is constructed.The results show that the accuracy for diagnosing and identifying 8 com-mon mechanical faults of 10 kV high-voltage circuit breaker by the method proposed in this paper is 99.99%.It can serve as an effective supplement to the current detection methods,improving the comprehensive detection and la-tent defect identification capabilities of high-voltage circuit breaker.关键词
10 kV高压断路器/声纹识别/融合特征/残差神经网络/故障诊断Key words
10 kV high-voltage circuit breaker/voiceprint recognition/fusion features/residual neural network/fault diagnosis引用本文复制引用
段梵,李先允,单光瑞,陈兰杭,杨凯..基于融合特征和残差神经网络的10 kV高压断路器机械故障声纹识别方法[J].高压电器,2025,61(3):205-213,9.基金项目
国网江苏省电力公司科技项目(J2023085).Project Supported by State Grid Jiangsu Electric Power Company Technology Project(J2023085). （J2023085）
