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首页> 外文期刊>Journal of control science and engineering >A Fault Diagnosis Method for Oil Well Pump Using Radial Basis Function Neural Network Combined with Modified Genetic Algorithm
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A Fault Diagnosis Method for Oil Well Pump Using Radial Basis Function Neural Network Combined with Modified Genetic Algorithm

机译：径向基神经网络与改进遗传算法相结合的油井泵故障诊断方法

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摘要

This paper presents a new method to diagnose oil well pump faults using a modified radial basis function neural network. With the development of submersible linear motor technology, rodless pumping units have been widely used in oil exploration. However, the ground indicator diagram method cannot be used to diagnose the working conditions of rodless pumping units because it is based on the load change of the polished rod suspension point and its displacement. To solve this problem, this paper presents a new method that is applicable to rodless oil pumps. The advantage of this new method is its use of a simple feature extraction method and advanced genetic algorithm to optimize the threshold and weight of the RBF neural network. In this paper, we extract the characteristic value from the operation parameters of the submersible linear motor and oil wellhead as the input vector of the fault diagnosis model. Through experimental analysis, the proposed method is proven to have good convergence performance, high accuracy, and high reliability.
机译：本文提出了一种使用改进的径向基函数神经网络诊断油井泵故障的新方法。随着潜水直线电机技术的发展，无杆抽油机已广泛用于石油勘探。但是，地面指示器图方法不能用于诊断无杆抽油机的工作状况，因为它是基于抛光杆悬挂点的载荷变化及其位移的。为了解决这个问题，本文提出了一种适用于无杆油泵的新方法。这种新方法的优点是使用简单的特征提取方法和高级遗传算法来优化RBF神经网络的阈值和权重。本文从潜水式直线电机和油井口的运行参数中提取特征值作为故障诊断模型的输入向量。通过实验分析，证明该方法具有良好的收敛性能，精度高，可靠性高。

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  • 来源
    《Journal of control science and engineering》 |2017年第2期|5710408.1-5710408.7|共7页
  • 作者

    Deliang Yu; Yanmei Li; Hao Sun; Yulong Ren; Yongming Zhang; Weigui Qi;

  • 作者单位

    Harbin University of Science and Technology, Harbin 150001, China,Research Institute of Oil Production Engineering, Daqing Oilfield Company, Daqing 163000, China;

    Harbin University of Science and Technology, Harbin 150001, China;

    Harbin University of Science and Technology, Harbin 150001, China;

    Harbin University of Science and Technology, Harbin 150001, China;

    Tongji University, Shanghai 200092, China;

    Harbin Institute of Technology, Harbin 150001, China;

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