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Application of CNN-LSTM in Gradual Changing Fault Diagnosis of Rod Pumping System

机译：CNN-LSTM在钢筋泵送系统逐步变化故障诊断中的应用

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摘要

Owing to the importance of rod pumping system fault detection using an indicator diagram, indicator diagram identification has been a challenging task in the computer-vision field. The gradual changing fault is a special type of fault because it is not clearly indicated in the indicator diagram at the onset of its occurrence and can only be identified when an irreversible damage in the well has been caused. In this paper, we proposed a new method that combines the convolutional neural network (CNN) and long short-term memory (LSTM) network to perform a gradual changing fault classification. In particular, we employed CNN to extract the indicator diagram multilevel abstraction features based on its hierarchical structure. We considered the change in the time series of indicator diagrams as a sequence and employed LSTM to perform recognition. Compared with traditional mathematical model diagnosis methods, CNN-LSTM overcame the limitations of the traditional mathematical model theoretical analysis such as unclear assumption conditions and improved the diagnosis accuracy. Finally, 1.3 million sets of well production were set as a training dataset and used to evaluate CNN-LSTM. The results demonstrated the effectiveness of utilizing CNN and LSTM to recognize a gradual changing fault using the indicator diagram and characteristic parameters. The accuracy reached 98.4%, and the loss was less than 0.9%.
机译：由于杆泵系统故障检测的重要性使用指示图，指示图识别是计算机视野中的一个具有挑战性的任务。逐渐变化的故障是一种特殊类型的故障，因为它在其发生的开始时未清楚地表明，只有在井中的不可逆损坏时才可以识别。在本文中，我们提出了一种结合卷积神经网络（CNN）和长短期存储器（LSTM）网络的新方法来执行逐渐变化的故障分类。特别是，我们使用CNN基于其层级结构提取指示图多级抽象功能。我们考虑了指示图的时间序列的变化作为序列，并采用LSTM执行识别。与传统的数学模型诊断方法相比，CNN-LSTM克服了传统数学模型理论分析的局限性，如不明确的假设条件，提高了诊断精度。最后，将130万套良好的生产设置为培训数据集，并用于评估CNN-LSTM。结果证明了利用CNN和LSTM使用指示图和特征参数来识别逐渐变化的故障的有效性。准确性达到98.4％，损失小于0.9％。

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  • 来源
    《Mathematical Problems in Engineering》 |2019年第22期|4203821.1-4203821.9|共9页
  • 作者

    He Yanfeng; Liu Yali; Shao Shuai; Zhao Xuhang; Liu Guojun; Kong Xiangji; Liu Lu;

  • 作者单位

    Changzhou Univ Sch Petr Engn Changzhou 213164 Jiangsu Peoples R China;

    Changzhou Univ Sch Petr Engn Changzhou 213164 Jiangsu Peoples R China;

    Changzhou Univ Sch Petr Engn Changzhou 213164 Jiangsu Peoples R China;

    Changzhou Univ Sch Petr Engn Changzhou 213164 Jiangsu Peoples R China;

    China Natl Oil & Gas Explorat & Dev Co Ltd Beijing 100034 Peoples R China;

    China Natl Oil & Gas Explorat & Dev Co Ltd Beijing 100034 Peoples R China;

    Changzhou Univ Sch Petr Engn Changzhou 213164 Jiangsu Peoples R China;

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