Fault diagnosis in rotating machinery

被引:0
|
作者
Lees, AW [1 ]
机构
[1] Univ Coll Swansea, Dept Engn Mech, Swansea SA2 8PP, W Glam, Wales
关键词
D O I
暂无
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
A tutorial discussion is given of some of the main faults which may be detected and diagnosed using observed vibrational data of a rotating machine. The paper is written with large turbo-machinery in view but many of the results discussed have relevance to other types of machine. The examination begins with the simplest, yet perhaps the most important, fault namely mass unbalance. The elementary procedure for locating the source of unbalance will be reviewed and procedures for balancing will be briefly summarised. The distinction between unbalance and a shaft bend will be discussed and the consequences of permanent and temporary bends will be examined. A fault which is sometimes connected with rotor bends is rubbing and the characteristics of this phenomena will be outlined including some recent developments in the theory and the classification of the different categories of rub which can occur in practice. An overview will be given of the characteristics of a cracked rotor and examples of cracks which have been detected using vibration measurements will be described. No discussion of the dynamics of large machines would be complete without a description of the effects of misalignment. Both static and dynamic types are discussed together with their consequences.
引用
收藏
页码:313 / 319
页数:7
相关论文
共 50 条
  • [41] Fault diagnosis of rotating machinery based on DVMD denoising
    Yin X.-L.
    Mu Z.-L.
    Wang Y.-Q.
    Kongzhi Lilun Yu Yingyong/Control Theory and Applications, 2022, 39 (07): : 1324 - 1334
  • [42] An explainable intelligence fault diagnosis framework for rotating machinery
    Yang, Daoguang
    Karimi, Hamid Reza
    Gelman, Len
    NEUROCOMPUTING, 2023, 541
  • [43] Application of Deep Learning in Fault Diagnosis of Rotating Machinery
    Jiang, Wanlu
    Wang, Chenyang
    Zou, Jiayun
    Zhang, Shuqing
    PROCESSES, 2021, 9 (06)
  • [44] Improved EEMD Applied to Rotating Machinery Fault Diagnosis
    Chen, Lue
    Tang, Geshi
    Zi, Yanyang
    Fan, Fei
    MEASURING TECHNOLOGY AND MECHATRONICS AUTOMATION IV, PTS 1 AND 2, 2012, 128-129 : 154 - +
  • [45] Coupling Fault Diagnosis of Rotating Machinery by Information Fusion
    Bai, Tangbo
    Zhang, Laibin
    Duan, Lixiang
    Wang, Jinjiang
    ANNUAL RELIABILITY AND MAINTAINABILITY SYMPOSIUM 2016 PROCEEDINGS, 2016,
  • [46] Application of neural networks in fault diagnosis of rotating machinery
    Qing, He
    Dongmei, Du
    Proceedings of the ASME Power Conference 2007, 2007, : 279 - 282
  • [47] An efficient method for imbalanced fault diagnosis of rotating machinery
    Yang, Jingli
    Yin, Shuangyan
    Gao, Tianyu
    MEASUREMENT SCIENCE AND TECHNOLOGY, 2021, 32 (11)
  • [48] Intrinsic component filtering for fault diagnosis of rotating machinery
    Zongzhen ZHANG
    Shunming LI
    Jiantao LU
    Yu XIN
    Huijie MA
    Chinese Journal of Aeronautics , 2021, (01) : 397 - 409
  • [49] Diagnosis of local fault and identification of transient fault force in rotating machinery
    Yao, Hongliang
    Li, He
    Li, Xiaopeng
    Wen, Bangchun
    Jixie Gongcheng Xuebao/Chinese Journal of Mechanical Engineering, 2007, 43 (01): : 120 - 124
  • [50] Diagnosis of local fault and identification of transient fault force in rotating machinery
    Yao, HL
    Yu, T
    Li, XP
    Han, QK
    Wen, BC
    DAMAGE ASSESSMENT OF STRUCTURES VI, 2005, 293-294 : 475 - 482