Holographic Spectrum in the Remote Rotating Machinery Fault Diagnosis

被引:1
|
作者
Tang, WuChu [1 ,2 ]
Shi, ZhiHui [1 ]
Kang, DaLi [3 ]
机构
[1] Dalian JiaoTong Univ, Sch Mech Engn, Dalian 116028, Peoples R China
[2] Dalian Univ Technol, Sch Mech Engn, Dalian 116024, Peoples R China
[3] Dalian Shenglilai Monitoring Tech Corp, Dalian 116024, Peoples R China
关键词
Rotating machinery; Fault diagnosis; Holographic spectrum; MD-Base database;
D O I
10.4028/www.scientific.net/AMM.130-134.2836
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
In this paper, a remote real-time online rotating machinery monitoring system, which was developed and tested in the Industrial application, is presented including its remote monitoring sub-system, fault diagnosis sub-system. First, the system architecture is introduced. Next, a web-based remote monitoring system is designed, which allows researches and engineers to remotely monitor the health of the machine from any remote geographic location through the Internet. Finally, the holographic spectrum technology in this system is discussed as well for the purpose of providing accurate and reliable diagnosis of the machine states and accident prevention.
引用
收藏
页码:2836 / +
页数:2
相关论文
共 50 条
  • [41] Fault Diagnosis of Rotating Machinery Based on the Multiscale Local Projection Method and Diagonal Slice Spectrum
    Lv, Yong
    Yuan, Rui
    Shi, Wei
    APPLIED SCIENCES-BASEL, 2018, 8 (04):
  • [42] Degree of cyclic target protrusion defined on squared envelope spectrum for rotating machinery fault diagnosis
    Yan, Xingyou
    Zhang, Heng
    Luo, Chong
    Miao, Qiang
    MEASUREMENT, 2022, 188
  • [43] Compound fault diagnosis of rotating machinery based on OVMD and a 1.5-dimension envelope spectrum
    Yan, Xiaoan
    Jia, Minping
    Xiang, Ling
    MEASUREMENT SCIENCE AND TECHNOLOGY, 2016, 27 (07)
  • [44] Fault diagnosis system of rotating machinery vibration signal
    You, Lei
    Hu, Jun
    Fang, Fang
    Duan, Lintao
    CEIS 2011, 2011, 15
  • [45] Artificial intelligence for fault diagnosis of rotating machinery: A review
    Liu, Ruonan
    Yang, Boyuan
    Zio, Enrico
    Chen, Xuefeng
    MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2018, 108 : 33 - 47
  • [46] Rotating machinery fault diagnosis based on fuzzy theory
    Lv, Z. (lvzhanjieyouxiang@163.com), 1600, Sila Science, University Mah Mekan Sok, No 24, Trabzon, Turkey (32):
  • [47] Generalized sparse filtering for rotating machinery fault diagnosis
    Cheng, Chun
    Hu, Yan
    Wang, Jinrui
    Liu, Haining
    Pecht, Michael
    JOURNAL OF SUPERCOMPUTING, 2021, 77 (04): : 3402 - 3421
  • [48] Thermal image based fault diagnosis for rotating machinery
    Janssens, Olivier
    Schulz, Raiko
    Slavkovikj, Viktor
    Stockman, Kurt
    Loccufier, Mia
    Van de Walle, Rik
    Van Hoecke, Sofie
    INFRARED PHYSICS & TECHNOLOGY, 2015, 73 : 78 - 87
  • [49] MPNet: A lightweight fault diagnosis network for rotating machinery
    Liu, Yi
    Chen, Ying
    Li, Xianguo
    Zhou, Xinyi
    Wu, Dongdong
    MEASUREMENT, 2025, 239
  • [50] Intrinsic component filtering for fault diagnosis of rotating machinery
    Zongzhen ZHANG
    Shunming LI
    Jiantao LU
    Yu XIN
    Huijie MA
    Chinese Journal of Aeronautics, 2021, 34 (01) : 397 - 409