高压电器2026，Vol.62Issue(2)：8-18,11.DOI:10.13296/j.1001-1609.hva.2026.02.002
基于时间卷积网络的GIS设备振动信号特征预测
Vibration Signal Feature Prediction of GIS Equipment Based on Temporal Convolution Network
摘要
Abstract
The variation of vibration signal of GIS equipment can reflect the mechanical condition inside the equip-ment.For improving the prediction accuracy of vibration signal characteristics of GIS equipment,in this paper a combined group measurement model based on decomposition-forecasting-reconstruction is proposed.First,based on historical vibration signals of GIS,vibration characteristic parameters are extracted in frequency domain by Fourier transform.Then,in order to eliminate as much as possible the influence due to the non-stationary characteristics of the vibration characteristic parameter sequence,the normalized sequence is decomposed by the variational mode decomposition(VMD)optimized by particle swarm optimization(PSO).Finally,the time convolution network(TCN)is used to predict a set of stationary modal components obtained by decomposition.The experimental results show that the root mean square error and the average absolute percentage error of the combined prediction model based on PSO-VMD-TCN proposed in this paper are 1.79%and 0.13%,respectively,which are superior to other methods in prediction accuracy and are conducive to the early fault diagnosis of GIS equipment.关键词
GIS设备/非平稳特性/预测模型/时间卷积网络/声振特征Key words
GIS equipment/non-stationary characteristic/prediction model/temporal convolutional network/vibroacoustic characteristics引用本文复制引用
王谦,蒋西平,龙英凯,张施令,胡东,赵仲勇,杨童亮..基于时间卷积网络的GIS设备振动信号特征预测[J].高压电器,2026,62(2):8-18,11.基金项目
重庆市留学人员回国创业创新支持计划项目(cx2019123). Project Supported by Chongqing Support Program for Overseas Students Returning to China(cx2019123). （cx2019123）
