Rod Pumping System Fault Diagnosis Based on Multi-Model Ensemble Method

Rod pumping systems are instrumental in the extraction of oil. Nevertheless, the complex operational environment increases their likelihood of malfunctions, thereby impacting both oil yield and the longevity of the equipment. To address the potential faults that may emerge within a rod pumping system, we propose a multi-model ensemble approach for fault diagnosis. The method utilizes models with 1 to 8 Encoder modules as base learners to extract deep-level features from the displacement and load condition data of the rod pumping system from different perspectives. Finally, the Dempster-Shafer(D-S) evidence theory is used in the decision-making component to integrate various feature information gleaned by the various models, facilitating a comprehensive fault diagnosis for the rod pumping system. In order to substantiate the efficacy of our proposed method, it is compared with traditional machine learning methods such as logistic regression and random forests, as well as Convolutional Neural Network(CNN) methods for images. A total of 8878 actual production operation data points are used for training and testing. Our method demonstrates a remarkable accuracy rate of 97.82%. This not only underscores its superior generalization capabilities but also highlights its capacity to significantly mitigate computational demands, offering good practical value.