摘要：The petroleum industry relies heavily on electric submersible pump (ESP) systems, which can cause substantial economic losses if malfunctioning. However, over half of these malfunctions are due to submersible electric motor (SEM) failures. To address this issue, motor current signature analysis (MCSA) is used in this work to monitor the condition of the SEM. This allows for further fault diagnosis of the ESP system, ensuring that malfunctions are detected and addressed promptly. In this study, a portable high-speed electrical signal acquisition tool was used to capture the current signal at a 10-KHz rate to monitor the SEM condition. Afterward, the fast Fourier transform (FFT) method was used to transform time-domain data into the frequency domain for spectrum analysis. Next, the motor rotational speed, which is critical for identifying fault characteristics, was obtained from the current spectrum based on the deep learning method, and the fault components were extracted from the spectrum to indicate various faults. Finally, maintenance decisions were made based on the detected failure characteristics to prevent further damage and improve system reliability. Fusing MCSA with deep learning techniques significantly improved fault diagnosis accuracy compared with MCSA alone, demonstrating the robustness and effectiveness of the proposed method. Copyright ? 2025 Society of Petroleum Engineers.

ISSN号：1086-055X

卷、期、页：卷30期5页2238-2255

发表日期：2025-05-01

影响因子：0.000000

期刊分区（SCI为中科院分区）：三区

收录情况：SCI（科学引文索引印刷版）,EI（工程索引）,SCIE（科学引文索引网络版）

发表期刊名称：SPE Journal

参与作者：Wang, Luting,Zhang, Zhuangzhuang

通讯作者：路鑫

第一作者：韩国庆,梁星原

论文类型：期刊论文

论文概要：路鑫,韩国庆,Wang, Luting,Zhang, Zhuangzhuang,梁星原，Fault Diagnosis of Electric Submersible Pump System Based on Motor Current Signal Analysis and Deep Learning Method，SPE Journal，2025，卷30期5页2238-2255

论文题目：Fault Diagnosis of Electric Submersible Pump System Based on Motor Current Signal Analysis and Deep Learning Method