基于相空间重构与改进GSA-SVM的高压断路器机械故障诊断

断路器机械部件传动、撞击产生的振动信号具有混沌特性,运用常规的信号处理方法很难分析其特性.首先采用互信息法和Cao算法将振动信号重构至高维空间后,计算其排列熵作为特征向量,输入支持向量机对断路器机械故障类型进行诊断,最后用粒子群算法(PSO)改进的万有引力搜索算法(GSA)混合算法优化支持向量机参数,利用断路器实测振动...

Mechanical Fault Diagnosis of High Voltage Circuit Breakers Based on Phase Space Reconstruction and Improved GSA-SVM

The vibration signals generated by transmission and impact of circuit breaker mechanical components have chaotic characteristics, which are difficult to be analyzed with conventional signal processing methods. Firstly, the vibration signals are reconstructed into a high-dimensional space by mutual information method and Cao algorithm, and the permutation entropy is calculated as the feature vector. And then the support vector machine (SVM) is used to identify the mechanical fault types of circuit breakers. Finally, the PSO improved GSA hybrid algorithm is used to optimize the parameters of SVM, and the measured vibration signals of the circuit breakers are used to verify the results. The results show that the characteristics of circuit breaker vibration signals can be accurately extracted with combination of phase space reconstruction and permutation entropy. The PSO-GSA-SVM can quickly and effectively identify the fault types of circuit breakers, thus providing an effective solution to such problems as path distortion, energy leakage and mode overlap of existing diagnosis methods.