首页|Fault Diagnosis Algorithm for Pumping Unit Based on Dual-Branch Time–Frequency Fusion

Fault Diagnosis Algorithm for Pumping Unit Based on Dual-Branch Time–Frequency Fusion

扫码查看
原文链接
  • NETL
  • NSTL
  • IEEE
The collected data of a pumping unit contain environmental noise, which significantly reduces the precision of fault diagnosis. The previous fault detection approach depends on manual feature extraction, which is time-consuming and laborious, and it cannot cope with high-noise conditions. Therefore, we propose a dual-branch time–frequency fusion deep learning model for fault diagnosis of the pumping unit. One branch extracts time-domain information, while the other branch extracts frequency-domain information by employing the fast Fourier transform. The branch information of these two branches is concatenated, and the gate-controlled channel transfer unit module automatically learns the competitive and cooperative relationships between each branch, making the key features more prominent in information fusion. Consequently, an accurate fault diagnosis of the pumping unit can be achieved under high-noise conditions. The results demonstrate that the proposed model outperforms the traditional schemes in terms of noise, with different signal-to-noise ratios.

Fault diagnosisNoiseOilsValvesFast Fourier transformsData miningConvolution

Fangfang Zhang、Yebin Li、Dongri Shan、Yuanhong Liu、Fengying Ma、Weiyong Yu

展开 >

School of Information and Automation Engineering, Qilu University of Technology(Shandong Academy of Sciences), Jinan, China

School of Mechanical Engineering, Qilu University of Technology(Shandong Academy of Sciences), Jinan, China|System Control and Information Processing Lab, Aerospace Information University, Jinan, China

School of Information and Electrical Engineering, Northeast Petroleum University, Daqing, China

Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, China

展开 >

2025

10.1109/TR.2024.3409427

IEEE transactions on reliability

IEEE transactions on reliability

ISSN：
年,卷(期)：2025.74(1)
  • 52