HOS-based nonparametric and parametric methodologies for machine fault detection

被引:56
作者
Chow, TWS [1 ]
Tan, HZ [1 ]
机构
[1] City Univ Hong Kong, Dept Elect Engn, Kowloon, Hong Kong, Peoples R China
关键词
ARMA models; blind identification; higher order statistics; machine fault detection; quadratic models; third-order cumulants; third-order spectra;
D O I
10.1109/41.873213
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
A framework for the detection and identification of machine faults through vibration measurements and higher order statistics (HOS) analysis is presented, As traditional signal processing techniques are based on the nonparametric magnitude analysis of vibration signals, in this paper, two different state-of-the-art HOS-based methods, namely, a nonparametric phase-analysis approach and a parametric linear or nonlinear modeling approach are used for machine fault diagnostic analysis. The focus of this paper is on the application of the techniques, not on the underlying theories. Each technique is described briefly and is accompanied by an experimental discussion on how it can be applied to classify the synthetic mechanical and electrical faults of induction machines compared with their normality. Promising results were obtained which show that the presented methodologies are possible approaches to perform effective preventive maintenance in rotating machinery.
引用
收藏
页码:1051 / 1059
页数:9
相关论文
共 32 条
  • [1] Blind system identification
    AbedMeraim, K
    Qui, WZ
    Hua, YB
    [J]. PROCEEDINGS OF THE IEEE, 1997, 85 (08) : 1310 - 1322
  • [2] MONITORING AND DIAGNOSIS OF ROLLING ELEMENT BEARINGS USING ARTIFICIAL NEURAL NETWORKS
    ALGUINDIGUE, IE
    LOSKIEWICZBUCZAK, A
    UHRIG, RE
    [J]. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 1993, 40 (02) : 209 - 217
  • [3] Arthur N, 1998, IEEE IND ELEC, P1895, DOI 10.1109/IECON.1998.723031
  • [4] Arthur N, 1998, IEEE IND ELEC, P1889, DOI 10.1109/IECON.1998.723030
  • [5] Arthur N, 1997, IEE CONF PUBL, P341, DOI 10.1049/cp:19971095
  • [6] Monitoring rotating tool wear using higher-order spectral features
    Barker, R.W.
    Klutke, G.
    Hinich, M.J.
    [J]. Journal of engineering for industry, 1993, 115 (01): : 23 - 29
  • [7] CHEN H. F., 1991, Identification and Stochastic Adaptive Control
  • [8] A NEURAL NETWORK APPROACH TO REAL-TIME CONDITION MONITORING OF INDUCTION-MOTORS
    CHOW, MY
    MANGUM, PM
    YEE, SO
    [J]. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 1991, 38 (06) : 448 - 453
  • [9] An accelerated recurrent network training algorithm using IIR filter model and recursive least squares method
    Chow, TWS
    Cho, SY
    [J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-FUNDAMENTAL THEORY AND APPLICATIONS, 1997, 44 (11): : 1082 - 1086
  • [10] Semiblind identification of nonminimum-phase ARMA models via order recursion with higher order cumulants
    Chow, TWS
    Tan, HZ
    [J]. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 1998, 45 (04) : 663 - 671