This paper reports the Grassberger-Procaccia algorithm and its modified version, with particular regard to how to reduce computation time, to avoid dynamic correlation, and to determine the scaling re gion. The influencing factors of correlation dimension computation of experimental data, especially the data length and noise level, are described comprehensively Results show that not theoretical consideration but the D2(m)—ln(r/r0) function should be used to determine the minimal requirement of data length of ex perimental data. In some cases, nonlinear noise reduction is necessary. Nonlinear time series analysis theory based on correlation dimension for practical application, especially for gearbox fault diagnosis, is introduced. Results show that the correlation dimension can usefully reflect the different kinematics mechanisms.
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