High Voltage Circuit Breaker Mechanical Fault Diagnosis with Local Time-frequency Singular Value and the Optimal Random Forest

ZHANG Xin; ZHANG Jing; GAO Xu; REN Xinzhuo; GU Chengtian; ZHU Weixiang

doi:10.13296/j.1001-1609.hva.2020.06.032

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High Voltage Circuit Breaker Mechanical Fault Diagnosis with Local Time-frequency Singular Value and the Optimal Random Forest

更新时间：2025-04-26

- High Voltage Circuit Breaker Mechanical Fault Diagnosis with Local Time-frequency Singular Value and the Optimal Random Forest
- High VoltageApparatus Vol. 56, Issue 6, Pages: 225-231(2020)
- 作者机构：
  
  1. 国网杭州供电公司
  2. 东北电力大学电气工程学院
- 作者简介：
- 基金信息：
- DOI：10.13296/j.1001-1609.hva.2020.06.032
  CLC： TM561
- Published：2020
- 稿件说明：
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[1]张欣,张静,高旭,任新卓,顾承天,朱玮翔.采用局部时—频奇异值与优化随机森林的高压断路器机械故障诊断[J].高压电器,2020,56(06):225-231.

ZHANG Xin, ZHANG Jing, GAO Xu, et al. High Voltage Circuit Breaker Mechanical Fault Diagnosis with Local Time-frequency Singular Value and the Optimal Random Forest[J]. High voltageapparatus, 2020, 56(6): 225-231.
[1]张欣,张静,高旭,任新卓,顾承天,朱玮翔.采用局部时—频奇异值与优化随机森林的高压断路器机械故障诊断[J].高压电器,2020,56(06):225-231. DOI： 10.13296/j.1001-1609.hva.2020.06.032.

ZHANG Xin, ZHANG Jing, GAO Xu, et al. High Voltage Circuit Breaker Mechanical Fault Diagnosis with Local Time-frequency Singular Value and the Optimal Random Forest[J]. High voltageapparatus, 2020, 56(6): 225-231. DOI： 10.13296/j.1001-1609.hva.2020.06.032.

摘要

为提高断路器机械振动信号的时—频特征分类能力、减小噪声干扰和提高断路器状态识别的准确性

提出一种基于S变换与优化随机森林算法的高压断路器机械故障诊断方法。首先

对高压断路器原始振动信号进行S变换;然后对S变换得到的时—频矩阵进行局部奇异值分解

以每个子矩阵的最大奇异值为特征向量;之后

将特征向量输入到随机森林中

以泛化误差与诊断准确率为综合指标对树的棵数进行寻优

构建最优随机森林分类器

最终实现对高压断路器机械故障状态的准确判别。对断路器实测振动数据开展对比实验

结果表明

新方法的特征类可分性好

整体故障识别准确性高。

Abstract

In order to improve the time-frequency feature classification ability of circuit breaker mechanical vibra-tion signals

reduce noise interference and improve the accuracy of state recognition of circuit breakers

a mechani-cal fault diagnosis method for high voltage circuit breaker based on S-transform and optimal random forest algorithmis proposed. Firstly

the vibration signal of high voltage circuit breaker is processed by S-transform;after that thetime-frequency matrix obtained by S-transform is decomposed by local singular value decomposition

and the maxi-mum singular value of each sub-matrix is the feature vector;Then

the feature vector is input into the random for-est

and the number of trees is synthesized by generalization error and diagnostic accuracy. The optimal random for-est classifier is constructed to realize the accurate identification of mechanical fault state of high voltage circuit break-er. Comparative experiment is carried out on the measured vibration data of circuit breaker

and the experimental re-sults show that the new method has good classification of features and high accuracy of fault identification.

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