Health Status Assessment and Prediction of 12 kV Vacuum Circuit Breaker Based on Acoustical Signal

MO Chu’an; TONG Xin; YANG Changzhou; LI Zhanfu; LI Yuandong

doi:10.13296/j.1001-1609.hva.2026.02.001

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Health Status Assessment and Prediction of 12 kV Vacuum Circuit Breaker Based on Acoustical Signal

Research & Analysis | 更新时间：2026-03-16

- Health Status Assessment and Prediction of 12 kV Vacuum Circuit Breaker Based on Acoustical Signal
- High Voltage Apparatus Vol. 62, Issue 2, Pages: 1-7(2026)
- 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：
- 基金信息：
- DOI：10.13296/j.1001-1609.hva.2026.02.001
  CLC：
- Received：20 July 2025，
  
  Revised：2025-10-11，
  
  Published：16 February 2026
- 稿件说明：
移动端阅览
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：

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.

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references

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