A lightweight convolutional neural network for working condition intelligent diagnosis of pumping units

Abstract

Accurate diagnosis of pumping unit working conditions and prompt fault resolution are vital for ensuring safe production in oilfields. In response to the limitations of traditional convolutional neural networks in diagnosing pumping unit working conditions, a lightweight convolutional neural network is proposed. This network addresses the challenges of shallow networks, which cannot effectively extract indicator diagram features, and deep networks, which suffer from excessive parameterization and high redundancy. Firstly, the combination of depthwise separable convolution and dilated convolution efficiently extracts the contour features of the indicator diagram while reducing the model’s parameter count. Additionally, the Attention Embedded Atrous Spatial Pyramid Pooling and the coordinate attention mechanism are incorporated to further capture the shape and position information of indicator diagram lines. The experimental results demonstrate that the proposed lightweight network has 1.26 M parameters and storage size of 4.82 M. Compared to ResNet-50, VGG-16, and MobileNet-V2, its parameter count and storage size are approximately 1/18, 1/11, and 1/2, respectively, making it easily deployable on hardware-limited diagnostic platforms. Moreover, with an average diagnostic accuracy of 98.17%, surpassing existing networks, it enables more effective diagnosis of pumping unit working conditions, thereby enhancing the reliability and accuracy of pumping unit operational monitoring.