基于示功图对抽油机井下工况进行自动诊断是数字油田不可或缺的环节。通常通过人工提取示功图的特征向量，然后输入机器学习算法分类器识别井下工况。然而，特征的选择需要借助经验和先验知识，并且直接影响后续分类器的最终性能。而人工提取特征易受知识干扰，且在特征提取的过程中存在信息丢失，这决定了识别结果的上限。为此，受深度神经网络自动特征提取的启发，提出基于卷积神经网络的示功图的离线训练与在线诊断的方法。首先将挑选后的信号数据转换为图像数据，然后将图像二值化降低计算复杂度，最后基于改进的LeNet-5网络探究最适合模型的网络结构。最终通过实验与目前先进的算法进行对比，验证了本方法的有效性和可行性。

Research on diagnostic method for working conditions of pumping unit wells based on deep learning

Abstract： It is an indispensable link to automatically diagnose downhole working conditions of pumping unit wells on dynamometer cards in digital oilfields. The feature vector of the dynamometer card is usually extracted manually and then identified downhole working conditions by the machine learning algorithm classifier as input. However, the selection of features requires experience and prior knowledge, then it directly affects the final performance of subsequent classifier. Features extracted manually are easily disturbed by knowledge, lead to the loss of key information, which determines the upper limit of the recognition result. Therefore, inspired by the automatic feature extraction of deep neural networks, this paper proposes the method of offline training and online diagnosis of dynamometer cards based on convolutional neural networks. Firstly the selected signal data is converted to image data. Then the image is binarized to reduce the complexity of computation. Finally based on the improved LeNet-5 network, we explore the network structure that is most suitable for the model. Compared with the current advanced algorithm, the validity and feasibility of this method are verified.

Key words : dynamometer card；downhole working conditions；automatic diagnosis；convolutional neural networks