基于声音信号的12 kV真空断路器健康状态评估与预测
Health Status Assessment and Prediction of 12 kV Vacuum Circuit Breaker Based on Acoustical Signal
- 高压电器 2026年62卷第2期 页码：1-7
- 作者机构：
福建工程学院福建省智能加工技术及装备重点实验室，福州 350118
福建工程学院福建省数控装备产业技术创新研究院，福州 350118
北京星网锐捷网络技术网络有限公司，北京 100036
- 作者简介：
莫楚安（1998—），男，硕士研究生，研究方向为断路器故障诊断与寿命评估（通信作者）（E-mail：425926274@qq.com）。
童昕（1964—），男，教授，博士，博士生导师，主要从事机电系统动态分析与控制、数字化分析与设计的研究（E-mail：xtong@fjut.edu.cn）。
- 基金信息：福建省重大专项专题项目;Major Special Project of Fujian Province(2014HZ0004-4);福建省自然科学基金资助项目;Natural Science Foundation of Fujian Province(2020J01870;2020J05180)
DOI：10.13296/j.1001-1609.hva.2026.02.001
中图分类号：收稿：2025-07-20，
修回：2025-10-11，
纸质出版：2026-02-16
- 稿件说明：
移动端阅览
莫楚安, 童昕, 杨长洲, 等. 基于声音信号的12 kV真空断路器健康状态评估与预测[J]. 高压电器, 2026,62(2):1-7.
MO Chu’an, TONG Xin, YANG Changzhou, et al. Health Status Assessment and Prediction of 12 kV Vacuum Circuit Breaker Based on Acoustical Signal[J]. High Voltage Apparatus, 2026, 62(2): 1-7.
莫楚安, 童昕, 杨长洲, 等. 基于声音信号的12 kV真空断路器健康状态评估与预测[J]. 高压电器, 2026,62(2):1-7. DOI： 10.13296/j.1001-1609.hva.2026.02.001.
MO Chu’an, TONG Xin, YANG Changzhou, et al. Health Status Assessment and Prediction of 12 kV Vacuum Circuit Breaker Based on Acoustical Signal[J]. High Voltage Apparatus, 2026, 62(2): 1-7. DOI： 10.13296/j.1001-1609.hva.2026.02.001.
摘要
为实现断路器的健康状态预测，提出了一种基于声音信号的断路器健康评估和预测方法。该方法采用经验模态算法（empirical mode decomposition，EMD）分解断路器的分闸声音信号得到多个本征模态函数（intrinsic mode functions，IMF）分量，通过核主成分（kernel principal component analysis，KPCA）分析方法融合多个本征模态函数分量的时域特征指标，进而建立断路器的综合健康指数（health indicator，HI）以表征断路器的运行健康状况。并通过构建一维卷积（1D-CNN）神经网络模型实现断路器的健康状态预测。最后以某12 kV户内真空断路器实验数据为例。结果显示，该方法构建的断路器HI曲线具有良好的单调性，能对断路器的健康状态进行较为准确的评估和预测。
Abstract
For achieving the health statu prediction of circuit breaker
a method for health assessment and prediction of circuit breaker based on acoustical signal analysis is proposed. This method adopts the empirical mode decomposition (EMD) algorithm to decompose the circuit breaker’s opening scoustical signal to obtain multiple intrinsic mode functions (IMF) component. The time-domain feature indicators of these IMFs are then fused using kernel principal component analysis (KPCA) to establish a comprehensive health indicator (HI) of the circuti breaker so to characterize the operational health condition of the circuit breaker. Additionally
a one-dimensional convolutional neural network (1D-CNN) model is set up to predict the health status of the circuit breaker. Finally
the experimental data from a 12 kV indoor vacuum circuit breaker is taken as an example. The result shows that the HI curve of the circuit breaker constructed by the method exhibits good monotonicity and can perform accurate ssessment and prediction on the health status of the circuit breaker.
关键词
Keywords
references
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