基于卷积神经网络的抽油机故障诊断

为提高抽油机的故障诊断性能、减少诊断模型的硬件存储,设计了基于轻量注意力卷积神经网络和示功图的故障诊断方法。首先,将示功图的位移-载荷数据转换为图像,诊断模型的基础结构采用深度分离卷积,提出一种可嵌入连续卷积层的正则化注意力模块,对每个卷积层的通道进行压缩、注意力计算,并根据注意力建立通道失活机制,输出具有特征抑制或加强的注意力特征图。其次,在模型学习算法上,提出注意力损失函数抑制易分样本对模型训练损失的贡献,使模型训练关注难分样本。最后通过仿真实验验证有效性,结果表明该模型硬件存储仅为5.4 MB,故障诊断精度达95.1%,满足抽油机工况检测的诊断精度要求。

Fault Diagnosis of Pumping Unit Based on Convolutional Neural Network

To improve the fault diagnosis accuracy of pumping unit and reduce the storage memory of diagnosis model, a novel fault diagnosis method based on a lightweight attention convolutional neural network is designed to recognize the dynamometer card in this paper. The shape outline composed by the displacement and load of dynamometer card is transformed into the image. Regarding the model architecture, we leverage the depthwise separable convolution and propose a regularization attention module which can be embedded to the consecutive convolution layers. Each channel of the depthwise separable convolution layer is compressed and filtered by the mechanism provided by the model. It constructs the attention feature map, in which the feature is suppressed or enhanced. Regarding the model training, the attention-based loss function is presented to suppress the contribution of easy samples to the training loss. It makes the training to pay more attention to hard samples than easy ones. Finally, the proposed method is evaluated by the experiment. The experimental results show that the size of the model is only 5.4 Mb, while the diagnosis accuracy is 95.1%, which meet the requirements of fault diagnosis of the pumping unit.