Order bispectrum: A new tool for reciprocated machine condition monitoring

被引:21
|
作者
Kocur, D [1 ]
Stanko, R [1 ]
机构
[1] Tech Univ Kosice, Dept Elect & Multimedial Commun, Fac Elect Engn & Informat, Kosice 04120, Slovakia
关键词
D O I
10.1006/mssp.2000.1307
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
Vibrations and sounds generated by reciprocated machines or by their parts strongly depend on the rotation speed of the main shaft of the tested reciprocating system. At the testing or at common performance of the reciprocated machines, their rotation speed is usually changing. With regard to this fact, signals produced by reciprocating machines are non-stationary ones. Therefore, conventional time-invariant methods of their spectral or bispectral analysis are frequently unable to provide meaningful results. In order to solve this problem in the field of polyspectral signal analysis, the order bispectrum analysis is proposed in this contribution. This approach is based on the bispectrum estimation from the signal which is a function of the angle of roll of the main shaft of reciprocated machine. A digital representation of this signal can be obtained by resampling of the signal conveniently sampled in the time domain. The advantages of the order bispectrum application in comparison with that of the conventional bispectrum approach is illustrated based on the example of an engine set classification. (C) 2000 Academic Press.
引用
收藏
页码:871 / 890
页数:20
相关论文
共 50 条
  • [1] Inverter fed induction machine condition monitoring using the bispectrum
    Arthur, N
    Penman, J
    PROCEEDINGS OF THE IEEE SIGNAL PROCESSING WORKSHOP ON HIGHER-ORDER STATISTICS, 1997, : 67 - 71
  • [2] Vibration — A tool for machine diagnostics and condition monitoring
    K N Gupta
    Sadhana, 1997, 22 : 393 - 410
  • [3] Vibration - A tool for machine diagnostics and condition monitoring
    Gupta, KN
    SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES, 1997, 22 (3): : 393 - 410
  • [4] Tool condition monitoring for cavity milling based on bispectrum analysis and Bayesian optimized SVM
    Li, Yuhang
    Wang, Guofeng
    Hu, Mantang
    Ma, Kaile
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2024, 133 (7-8): : 3873 - 3889
  • [5] Condition monitoring of a Nd:YAG laser machine tool
    Tonshoff, HK
    Graumann, C
    Jennings, AD
    Kral, V
    LASER METROLOGY AND MACHINE PERFORMANCE III, 1997, : 55 - 64
  • [6] Fingerprint analysis for machine tool health condition monitoring
    Fogliazza, Giuseppe
    Arvedi, Camillo
    Spoto, Calogero
    Trappa, Luca
    Garghetti, Federica
    Grasso, Marco
    Colosimo, Bianca Maria
    IFAC PAPERSONLINE, 2021, 54 (01): : 1212 - 1217
  • [7] Condition Monitoring of Machine Tool Feed Drives: A Review
    Butler, Quade
    Ziada, Youssef
    Stephenson, David
    Gadsden, S. Andrew
    JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME, 2022, 144 (10):
  • [8] A review of machine vision sensors for tool condition monitoring
    Kurada, S
    Bradley, C
    COMPUTERS IN INDUSTRY, 1997, 34 (01) : 55 - 72
  • [9] Review of machine vision sensors for tool condition monitoring
    Univ of Victoria, Victoria, Canada
    Comput Ind, 1 (55-72):
  • [10] Application of Machine Learning for Tool Condition Monitoring in Turning
    Patange, A. D.
    Jegadeeshwaran, R.
    Bajaj, N. S.
    Khairnar, A. N.
    Gavade, N. A.
    SOUND AND VIBRATION, 2022, 56 (02): : 127 - 145