基于SA-PSO的煤矿小功率电机故障诊断研究

Research on Fault Diagnosis of Small Power Motor in Coal Mine Based on SA-PSO

小功率电机广泛应用于煤矿带式输送机辅机、水处理系统等场合。为准确诊断小功率电机运行过程中发生的常见故障,提出一种模拟退火粒子群优化(SA-PSO)算法优化最小二乘支持向量机(LSSVM)的故障诊断方法。首先利用RELAX算法剔除定子电流基波频率分量,然后使用小波包分解对信号进行分解和重构,选取重构后特定频带的能量值为特征信号,最后用SA-PSOLSSVM模型进行故障分类。实验结果表明,该方法在小功率电机故障诊断上有较好的诊断精度。

Small power motors are widely used in coal mine belt conveyor auxiliary equipment,water treatment systems and other occasions.In order to accurately diagnose common faults that occur during the operation of smallpower motor,proposed a fault diagnosis method which uses simulated annealing particle swarm optimization(SA-PSO)algorithm to optimize least squares support vector machine(LSSVM).Firstly,RELAX algorithm was used to remove the fundamental frequency component of stator current.Then,wavelet packet decomposition was used to decompose and reconstruct the signal,and the energy value of a specific frequency band after reconstruction was selected as the feature signal.Finally,the SA-PSO-LSSVM model was used for fault classification,and the experimental results show that this method has good diagnostic accuracy in the diagnosis of small power motor fault.