MA Hailong, WANG Jun. Connectivity fault diagnosis method of motor and its applicatio[J]. Journal of Mine Automation, 2017, 43(4): 77-81. DOI: 10.13272/j.issn.1671-251x.2017.04.018
Citation: MA Hailong, WANG Jun. Connectivity fault diagnosis method of motor and its applicatio[J]. Journal of Mine Automation, 2017, 43(4): 77-81. DOI: 10.13272/j.issn.1671-251x.2017.04.018

Connectivity fault diagnosis method of motor and its applicatio

More Information
  • In view of problem that connectivity fault feature of motor was identified difficultly, on the basis of mathematic models and spectrum characteristic of motor connectivity faults represented by misalignment, looseness and base stiffness, empirical mode decomposition method was proposed to process vibration signal of motor. Diagnosis result is gotten according to fault feature frequency. The field application results verify effectiveness of the method.
  • Related Articles

    [1]LI Yingna, CUI Yanping, AN Boshuo, LIU Baijian, JIN Jianwei. Research on the roadheader cutting control system based on convolutional neural network and fuzzy PID[J]. Journal of Mine Automation, 2025, 51(1): 61-70, 137. DOI: 10.13272/j.issn.1671-251x.2024070084
    [2]SHI Zhiyuan, TENG Hu, MA Chi. Fault diagnosis of planetary gearbox based on multi-information fusion and convolutional neural network[J]. Journal of Mine Automation, 2022, 48(9): 56-62. DOI: 10.13272/j.issn.1671-251x.2022060011
    [3]YAN Honglin. Coal and gangue image classification model based on improved feedback neural network[J]. Journal of Mine Automation, 2022, 48(8): 50-55, 113. DOI: 10.13272/j.issn.1671-251x.2022050026
    [4]WANG Anyi, ZHOU Xiaoming. Mine roadway field strength prediction based on improved convolutional neural network[J]. Journal of Mine Automation, 2021, 47(10): 49-53. DOI: 10.13272/j.issn.1671-251x.2021030073
    [5]HUANG Chongqia. Fault identification of rolling bearing based on multi hidden layers wavelet convolution extreme learning neural network[J]. Journal of Mine Automation, 2021, 47(5): 77-82. DOI: 10.13272/j.issn.1671-251x.2020110036
    [6]RAO Zhongyu, WU Jingtao, LI Ming. Coal-gangue image classification method[J]. Journal of Mine Automation, 2020, 46(3): 69-73. DOI: 10.13272/j.issn.1671-251x.17495
    [7]TANG Shiyu, ZHU Aichun, ZHANG Sai, CAO Qingfeng, CUI Ran, HUA Gang. Target detection of underground personnel based on deep convolutional neural network[J]. Journal of Mine Automation, 2018, 44(11): 32-36. DOI: 10.13272/j.issn.1671—251x.2018050068
    [8]DU Yun, ZHANG Lulu, PAN Tao. Miners' facial expression recognition method based on convolutional neural network[J]. Journal of Mine Automation, 2018, 44(5): 95-99. DOI: 10.13272/j.issn.1671-251x.17312
    [9]WANG Chong-li. Model XBSG-6/50 Arc Suppression Coil of Automatic Tracking and Compensatio[J]. Journal of Mine Automation, 1995, 21(3): 33-35.
  • Cited by

    Periodical cited type(2)

    1. 杨乐,何大阔,王正松. 基于机器视觉的二维码检测教学实验系统设计. 控制工程. 2024(09): 1722-1728 .
    2. 杨乐,周子振,蔡恩文,王正松. 包装印刷品二维码在线检测系统设计. 包装与食品机械. 2023(04): 90-95 .

    Other cited types(4)

Catalog

    WANG Jun

    1. On this Site
    2. On Google Scholar
    3. On PubMed

    Article Metrics

    Article views (69) PDF downloads (11) Cited by(6)
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return