摘要
电流卡片是电潜泵井故障诊断的主要依据,目前主要由技术人员人工完成对电泵井的诊断,难以实现快速大批量诊断;且诊断结果受工程师技术水平影响较大。因此提出了应用BP神经网络进行电潜泵故障的诊断,首先大量收集不同泵况的电流卡片,建立样本库;然后提取样本库中不同泵况电流卡片的特征值,按照一定的训练原则进行训练。训练完成后得到所需的权值矩阵,将要诊断的电流卡片特征值与权值矩阵进行计算得出相似度。通过计算机编程应用,证明该方法可以准确、快速地进行电泵诊断。
Current card is the main basis of electric submersible pump well fault diagnosis. At present, the diagnosis of electric pumping wells is mainly completed by technical workers. It is difficult to achieve rapid mass diagnosis, and the diagnosis results are greatly influenced by engineer technology level. Therefore the application of BP neural network for fault diagnosis of electric submersible pump is put forward. First of all, different pump current cards are collected, and sample library is established. The eigenvalues of the different pump current card are gathered and trained according to certain principles. After completion of training a weight matrix is got, and the current card characteristic values and weights matrix of similarity is calculated. Through computer programming application, the method can be proved to make electric pump diagnosis accurately and rapidly.
出处
《石油化工高等学校学报》
CAS
2016年第1期76-79,共4页
Journal of Petrochemical Universities
关键词
潜油电泵
电流卡片
BP神经网络
特征值
工况诊断
Electric submersible pump
Current card
BP neural network
Eigenvalue
Working condition diagnosis
