A novel method for trajectory recognition and working condition diagnosis of sucker rod pumping systems based on high-resolution representation learning

Abstract

The pumping unit is the most important lifting equipment in the rod pumping system, which transmits ground power to the downhole pump through its sucker rod. Trajectory identification of the rod pumping system can be used to study the motion law of the pumping unit, and it is the basis for the design of the pumping parameters and the analysis of the working conditions. Most of the studies on the dynamics of the pumping unit are to discuss the simple harmonic motion of the suspension point or the motion of the crank-slider in the ideal state, which is quite different from the actual situation. In this paper, a key point detection method for oil pumping units based on high-resolution representation learning is proposed. The model adopts a multi-resolution parallel network, which connects multiple network branches with different resolutions in parallel. Then it performs multi-stage processing on the input pumping unit images and outputs the predicted Gaussian heatmaps of key points to realize the trajectory recognition of the pumping unit. Different from the traditional series convolutional network structure, which needs to recover high resolution from low resolution, the parallel structure of this model is able to maintain the high resolution of the backbone network and thus the learned representations are more accurate in spatial location. This method is also experimentally verified and compared with the latest convolutional network structure. The results show that the model accuracy of the high-resolution representation learning method is more than 6% higher than that of the comparison methods. In addition, the results of this model can be used to judge the balance of the pumping unit, automatically calculate the maximum stroke and polished rod stroke, and have guiding significance for the working condition diagnosis of the rod pumping system.