Research Progress and Prospects in Fault Diagnosis of Pumping Units Using Dynamometer Cards

During oilfield development, the pumping unit serves as a critical piece of equipment, typically operating under sustained high loads that lead to frequent failures. These failures adversely affect the production efficiency and result in significant economic losses. Consequently, conducting efficient diagnostic research on pumping unit failures is of paramount importance to ensure the stability of oil production. To advance technological developments in this field, this paper provides a comprehensive review and synthesis of related studies. Firstly, the progress in feature extraction methods from dynamometer card is examined, with a particular emphasis on the effectiveness of techniques such as Freeman chain code, Fourier descriptors, gray-level co-occurrence matrices, moment eigenvector, and curve moments. Secondly, the application of classical machine learning algorithms and deep learning algorithms in pumping unit fault diagnosis is thoroughly investigated. This analysis delves into the advantages, limitations, and applicable scopes of algorithms including support vector machine (SVM), back propagation (BP) neural networks, generative adversarial network (GAN), and convolutional neural network (CNN). Finally, the paper anticipates future research directions, highlighting the optimization and innovation of feature extraction methods and the deep integration of multi-source data to enhance diagnostic accuracy. Advancements in these areas are expected to facilitate the intelligent and automated diagnosis of pumping unit faults, thereby providing reliable technical support for efficient and safe oilfield production. It is anticipated that this review will serve as a valuable reference for researchers in related fields and inspire further in-depth investigation.