CAJ | 학술논문

该文将神经网络应用于抽油系统的故障诊断，根据泵功图的几何特征提取特征值作为BP神经网络的输入信号，利用自适应性以及线性映射能力，建立抽油系统输入的故障信息与输出的故障类型间的映射。通过对大量故障样本的学习将知识以权值和阈值的形式存储于网格中，最终输出抽油系统的故障类型。通过实例分析，模型具有比较高的准确性和可行性。
해문장신경망락응용우추유계통적고장진단，근거빙공도적궤하특정제취특정치작위BP신경망락적수입신호，이용자괄응성이급선성영사능력，건립추유계통수입적고장신식여수출적고장류형간적영사。통과대대량고장양본적학습장지식이권치화역치적형식존저우망격중，최종수출추유계통적고장류형。통과실례분석，모형구유비교고적준학성화가행성。
Neural network is used in the fault diagnosis of suck rod pumping system .The characteristic value is obtained based the geometric characteristic of the pump dynamometer card as input signals of back propagation( BP) neural networks .The relations between the network fault information and fault patterns are established utilizing the self-adaptation and nonlinearity mapping functions of the neural network . The knowledge in nets is kept in the form of weigh and threshold by learning from the fault samples , and the outputs of nets are typical models of the fault .After numerical analysis , the results indicate the feasibility and veracity of this method .