164 / 2025-06-09 00:52:20

A Series of Structural Head Loop Bottleneck Attention in Fault Diagnosis for Pumping Unit

fault diagnosis,indicator diagram,deep learning,attention mechanism

Oil extraction is a crucial component of the petroleum industry, and oil extractors play a key role in this process. However, the operation and maintenance of oil extractors face numerous challenges such as the dispersed distribution of equipment across vast oil fields and the frequent occurrence of various mechanical faults. These issues not only reduce operational efficiency, but also increase maintenance costs and downtime. In addition, with the increasing complexity and diversity of faults, traditional manual identification methods are becoming inadequate to meet the growing demands of the industry. Manual approaches are often time-consuming, error-prone, and require experienced personnel, which limits their scalability and effectiveness in large-scale operations. To address these challenges, this paper proposes a novel method for fault diagnosis of oil extractors. Firstly, the paper collects a multi-class dataset, which contains more than 3k images and 8 different types of faults. Secondly, a fault diagnosis model is introduced, which incorporates a series of attention modules to enhance feature extraction and improve the accuracy of the fault diagnosis. Finally, comparative experiments demonstrate that the proposed method significantly improves classification accuracy. This method shows potential for practical applications, offering a reliable solution for fault diagnosis in the oil extraction industry.