电子器件2024，Vol.47Issue(1)：176-181,6.DOI:10.3969/j.issn.1005-9490.2024.01.030
基于卷积神经网络的放电声音故障检测
Fault Detection of Discharge Sound Based on Convolutional Neural Network
曾锃 1张震 1缪巍巍 1李凤强 1张明轩 1谢跃2
作者信息
- 1. 国网江苏省电力公司信息通信分公司,江苏 南京 210024
- 2. 南京工程学院信息与通信工程学院,江苏 南京 211167
- 折叠
摘要
Abstract
A discharge sound detection method based on convolution neural network is proposed.Aiming at the partial discharge phe-nomenon caused by equipment insulation aging in power system,the acoustic signal detection method of terminal edge node is proposed to monitor the normal operation,partial discharge and fault status of the equipment in real time,and feed back the abnormal state to the operation and maintenance center through the edge calculation network.The system collects the audio data of discharge when the fault occurs through the edge node of the device terminal.These faults include normal operation,partial discharge and the state of the fault being occurred.The signal preprocessing and extraction can reflect the fault state of audio features.Then,the processed data are used as the input of the recognition model constructed by the convolutional neural networks.Experiments show that the average recognition rate of the proposed method is about 2%higher than that of classical deep neural network.关键词
卷积神经网络/深度学习/特征提取/信号检测Key words
convolution neural network/deep learning/feature extraction/signal detection分类
信息技术与安全科学引用本文复制引用
曾锃,张震,缪巍巍,李凤强,张明轩,谢跃..基于卷积神经网络的放电声音故障检测[J].电子器件,2024,47(1):176-181,6.
