隋先富,王彪,范白涛,于继飞,刘兆年,吕彦平. 基于人工神经网络的电潜泵电流卡片快速模式识别[J]. 石油钻采工艺,2021,43(2):217-225. DOI: 10.13639/j.odpt.2021.02.013
引用本文: 隋先富,王彪,范白涛,于继飞,刘兆年,吕彦平. 基于人工神经网络的电潜泵电流卡片快速模式识别[J]. 石油钻采工艺,2021,43(2):217-225. DOI: 10.13639/j.odpt.2021.02.013
SUI Xianfu, WANG Biao, FAN Baitao, YU Jifei, LIU Zhaonian, LYU Yanping. Artificial neural network based fast pattern recognition of ESP ammeter card[J]. Oil Drilling & Production Technology, 2021, 43(2): 217-225. DOI: 10.13639/j.odpt.2021.02.013
Citation: SUI Xianfu, WANG Biao, FAN Baitao, YU Jifei, LIU Zhaonian, LYU Yanping. Artificial neural network based fast pattern recognition of ESP ammeter card[J]. Oil Drilling & Production Technology, 2021, 43(2): 217-225. DOI: 10.13639/j.odpt.2021.02.013

基于人工神经网络的电潜泵电流卡片快速模式识别

Artificial neural network based fast pattern recognition of ESP ammeter card

  • 摘要: 电流卡片诊断是一种典型的电潜泵工况诊断手段,传统的电流卡片模式识别需要人工完成,存在技术壁垒,会引入主观误差。人工神经网络作为一种机器学习算法,能够弥补人工识别的误差。为实现快速、准确而且客观的模式识别。通过对收集到的电流卡片经过数据预处理后得到的电流数据特征值与实际工况的对应关系,建立人工神经网络模型。利用建立的人工神经网络模型进行工况诊断,具有传统电流卡片人工识别不可比拟的优点。通过采用上述方法搭建人工神经网络模型并进行模式识别,通过提取未参与训练的井的电流卡片数据对工况诊断模型进行验证,达到了较高的准确率,证明了使用人工神经网络对电流卡片进行快速模式识别、实现工况诊断的可行性和可靠性。

     

    Abstract: Ammeter card diagnosis is one typical means to diagnose the working conditions of electrical submersible pump (ESP). The tradition pattern recognition of ammeter card needs artificial operation and has technical barriers, so subjective error can be introduced. As a kind of machine learning algorithm, artificial neural network can make up for the error of artificial recognition. In order to realize fast, accurate and objective pattern recognition, this paper established the artificial neural network model based on the corresponding relationship between the actual working condition and the current data characteristic value obtained after the data pretreatment of the collected ammeter cards. Then, this artificial neural network model was applied to working condition diagnosis. It is indicated that the artificial neural network model is absolutely more advantageous than the traditional artificial recognition of ammeter card. An artificial neural network model was established by means of above mentioned method and applied to pattern recognition. The working condition diagnosis model was verified by extracting the ammeter card data of untrained wells, which indicates higher accuracy. The research results indicate the feasibility and reliability of artificial neural network to fast pattern recognition of ammeter card and working condition diagnosis.

     

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