Research on Fault Analysis Model of Lightweight Pumping Unit Based on Classical Convolutional Neural Network

被引：2
|
作者：
Han, Chuanjun [1 ]
Zhou, Xinlie [1 ]
Fan, Chunming [2 ]
Zheng, Jiawei [2 ]
机构：
[1] Southwest Petr Univ, Sch Mech Engn, Chengdu, Peoples R China
[2] CNPC Baoji Oilfield Machinery Co LTD, Chengdu Res Ctr, Chengdu, Peoples R China
来源：
JOURNAL OF FAILURE ANALYSIS AND PREVENTION | 2023年 / 23卷 / 06期
关键词：
Fault diagnosis; Pumping unit; Neural network; Lightweight; DIAGNOSIS;
D O I：
10.1007/s11668-023-01776-8
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
In the conventional sucker rod pumping system, the pumping unit often be produced many types of faults that due to the influence of sucker rod, pump, and other accessories, as well as oil well paraffinication, gas interference, sand production and other environmental impacts. Using indicator diagram to analyze the fault diagnosis of pumping units is a common method. In this paper, a lightweight model was designed based on the classical convolutional neural network, and a comparative experiment was used to optimize the model from four perspectives: learning rate, convolution kernel size, batch size, and optimization algorithm. Finally, the average accuracy achieved 95.5%.
引用
收藏
页码：2402 / 2415
页数：14
相关论文
共 50 条
  • [1] Research on Fault Analysis Model of Lightweight Pumping Unit Based on Classical Convolutional Neural Network
    Chuanjun Han
    Xinlie Zhou
    Chunming Fan
    Jiawei Zheng
    Journal of Failure Analysis and Prevention, 2023, 23 : 2402 - 2415
  • [2] Fault Diagnosis of Pumping Unit Based on Convolutional Neural Network
    Du J.
    Liu Z.-G.
    Song K.-P.
    Yang E.-L.
    Dianzi Keji Daxue Xuebao/Journal of the University of Electronic Science and Technology of China, 2020, 49 (05): : 751 - 757
  • [3] Research on fault diagnosis of rolling bearing based on lightweight convolutional neural network
    Zhang, Xiaochen
    Li, Hanwen
    Meng, Weiying
    Liu, Yaofeng
    Zhou, Peng
    He, Cai
    Zhao, Qingbo
    JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING, 2022, 44 (10)
  • [4] Research on fault diagnosis of rolling bearing based on lightweight convolutional neural network
    Xiaochen Zhang
    Hanwen Li
    Weiying Meng
    Yaofeng Liu
    Peng Zhou
    Cai He
    Qingbo Zhao
    Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2022, 44
  • [5] Research on Fault Diagnosis Model of Convolutional Neural Network Based on Signal Decomposition
    Wang, Sen
    Li, Peng
    Niu, Wei
    2021 IEEE/ACIS 21ST INTERNATIONAL FALL CONFERENCE ON COMPUTER AND INFORMATION SCIENCE (ICIS 2021-FALL), 2021, : 97 - 102
  • [6] LMFRNet: A Lightweight Convolutional Neural Network Model for Image Analysis
    Wan, Guangquan
    Yao, Lan
    ELECTRONICS, 2024, 13 (01)
  • [7] Series Arc Fault Identification Method Based on Lightweight Convolutional Neural Network
    Tang, Aixia
    Wang, Zhiyong
    Tian, Shigang
    Gao, Hongxin
    Gao, Yong
    Guo, Fengyi
    IEEE ACCESS, 2024, 12 : 5851 - 5863
  • [8] Rotating machinery fault diagnosis based on a novel lightweight convolutional neural network
    Yan, Jing
    Liu, Tingliang
    Ye, Xinyu
    Jing, Qianzhen
    Dai, Yuannan
    PLOS ONE, 2021, 16 (08):
  • [9] Power Analysis Attack Based on Lightweight Convolutional Neural Network
    Li, Xiang
    Yang, Ning
    Chen, Aidong
    Liu, Weifeng
    Liu, Xiaoxiao
    Huang, Na
    FRONTIERS IN CYBER SECURITY, FCS 2022, 2022, 1726 : 105 - 118
  • [10] Research on Fault Diagnosis Algorithm Based on Convolutional Neural Network
    Li, Xiaolong
    Wang, Sen
    Zhou, Wei
    Huang, Qi
    Feng, Bowen
    Liu, Lilan
    2019 11TH INTERNATIONAL CONFERENCE ON INTELLIGENT HUMAN-MACHINE SYSTEMS AND CYBERNETICS (IHMSC 2019), VOL 1, 2019, : 8 - 12
12345