基于混沌-懒蚂蚁SVM的抽油机故障识别

Fault Identification of Pumper Based on Chaos-Idle Ant SVM

由于抽油机故障诊断因故障种类繁多且系统复杂而导致识别准确率低,增加了故障诊断难度,因此根据SVM(Support Vector Machine)工作原理,应用蚁群算法并用其调整SVM的惩罚系数以及核函数参数,为避免蚁群算法陷入局部最优解,通过引入懒蚂蚁策略,在蚁群算法停滞后利用懒蚂蚁再次更新信息素从而使蚁群获取新的路径。为进一步降低蚁群算法出现局部最优解的问题并提高蚁群算法普通蚂蚁个体在寻优初期的搜索速度,通过利用混沌初始化和扰动优化懒蚂蚁使其具有更好的全局寻优特性。并利用抽油机井的测试数据作为检验该故障诊断系统的样本数据,实验结果表明,该故障诊断系统具有较高的故障识别准确率。

Failure diagnosis of oil pumping machine has low identification accuracy due to various faults and complex system,which increases the difficulty of fault diagnosis.After clarifying the working principle of SVM,the ant colony algorithm is carefully studied to adjust the penalty coefficient of SVM(Support Vector Machine)and the kernel function parameters.The ant colony algorithm has the problem of easy to fall into the local optimal solution,which introduces the idle ant to update the pheromone again after the ant colony algorithm fails to enable the ant group to obtain new paths.In order to further reduce the problem of local optimal solution of ant colony algorithm and improve the search speed of ordinary ants in the early stage of optimization,idle ants are optimized by using chaotic initialization and chaotic perturbation.The test data of the pumping machine shows that the proposed fault diagnosis system has high fault identification accuracy.