基于EWT和多尺度熵的高压断路器故障诊断

提出了一种以经验小波变换(empirical wavelet transform,简称EWT)和多尺度熵相结合的高压断路器振动信号的特征向量提取和故障诊断的分析方法。首先,将高压断路器的振动信号进行经验小波变换,得到内禀模态函数(intrinsic mode function,简称IMF),选择相关系数较大的IMF进行重构;其次,提取重构信号的多尺度熵作为表征断路器状态的特征向量,采用归一化的方法对特征向量进行预处理并以此作为支持向量机(support vector machine,简称SVM)的输入向量进行分类训练;最后,将测试样本信号故障特征输入训练好的SVM,在SVM核函数参数进行网格算法优化的基础上进行状态识别及分类。实验结果表明,该方法可快速准确地检测高压断路器故障,实现了断路器故障的状态识别。

Fault Diagnosis for High Voltage Circuit Breakers Based on EWT and Multi-scale Entropy

A new method for fault diagnosis of high voltage circuit breakers is proposed based on empirical wavelet transform (EWT) and multi-scale entropy. Firstly, the original vibration signals of high voltage circuit breakers are decomposed into a number of intrinsic mode functions by EWT method, reconstructing the signal with intrinsic mode function (IMF) whose correlation coefficient is bigger than others. Secondly, the vector that stands for circuit breaker working condition is extracted from reconstructed signals based on the mult-scale entropy. The feature vector is preprocessing with the method of normalization considered as the input vector of support vector machine. Lastly, based on grid algorithm optimization of its kernel functions, the support vector machine can classify the different states of the circuit breaker after importing the feature vector of test sample. The experimental results indicate that the method can fast and accurately diagnose breaker faults, and identify the states of circuit breaker.