LW30-252型SF 6高压断路器潜伏性故障声信号识别方法

Latent Fault Identification Method of LW30-252 SF 6 HV Circuit Breaker Operating Mechanism Acoustic Signal

高压断路器动作声信号中包含其本体的机械状态信息。以LW30-252型SF 6高压断路器的CT26弹簧操动机构为研究对象,搭建故障模拟平台,模拟了高压断路器油缓冲器漏油、合闸弹簧疲劳、传动轴销磨损、主轴卡涩、地脚螺栓松动共5种典型潜伏性故障,然后以断路器动作的声音为检测信号,提取声信号的梅尔倒谱系数、伽马通滤波倒谱系数与幂律归一化倒谱系数共同构成混合倒谱系数,输入卷积神经网络进行故障识别,并在实测的断路器潜伏性故障声纹数据集上进行了验证,结果表明本文方法能够有效实现断路器的5种潜伏性机械故障诊断。

The action acoustic signal of high voltage circuit breaker contains the mechanical state information of its mechanical structure.Taking the CT26 spring operating mechanism of LW30-252 SF 6 high voltage circuit breaker as the research object,this paper built a fault simulation platform to simulate five typical latent faults of high voltage circuit breaker,including oil leakage of oil buffer,fatigue of closing spring,wear of transmission shaft pin,jamming of main shaft and loosening of anchor bolt.With the sound of circuit breaker action as the detection signal,the Mel Frequency Cepstral Coefficient,Gammatone Filter Cepstral Coefficient and Power-Normalized Cepstral Coefficient of acoustic signal were extracted to construct the Mixed Cepstral Coefficient,which was input into convolution neural network for fault identification.The method was verified on the measured latent fault voiceprint data set of circuit breaker.The results show that this method can realize the voiceprint diagnosis of five kinds of latent mechanical faults of circuit breaker.