示功图与模糊神经网络结合的抽油机故障诊断

Diagnosis of Pumping Unit with Combing Indicator Diagram with Fuzzy Neural Networks

随着石油工业的发展,采油工艺的提高,抽油机故障诊断在生产环节中尤为重要,对分析传统示功图对抽油机故障诊断不足进行了分析,其不足主要集中在诊断分析方式属于定性分析,并且诊断维度过于单一.首先对抽油机的故障进行了总结,并引入模糊神经网络,并在模糊神经网络中引入黄金分割法的变步长BP算法实现推理过程,通过模糊神经网络与示功图特征综合评判其故障生成诊断方案用来实现从不同维度解决抽油机故障方面的问题,同时本文通过仿真实验验证了该理论的可行性.

With the development of petroleum industry and the improvement of oil recovery technology, the fault diagnosis of pumping unit is particularly important in the production process. The deficiencies in diagnosis of traditional indicator diagram towards pumping unit are analyzed in the paper. The main insufficiencies are focused on the diagnosis and analytic methods which are the qualitative analysis, and the diagnosis dimension is too limited. The faults of pumping unit are summarized in this paper. Meanwhile, the fuzzy neural network is introducedand the reasoning process is achieved by introducing a step-changing BP algorithm based on gold-segmentation is given. From the different dimensions to solve the problems of pumping unit fault it creates a diagnosis scheme referring to the characteristics of the fuzzy neural network, and the indicator diagram to comprehensively evaluate the faults. Finally, the theoretical feasibility is verified through the simulation experiments.