悬架-车轮系统隔振参数选择的正确与否取决于系统振动信号周期性的好坏，而单纯用肉眼无法有效判别振动信号周期性的差别。用Grassberger-Procaccia（G-P）算法和小数据量法合理选择嵌入维数、延迟时间和序列平均周期等重要参数，并且在对数曲线图中准确划定无标度区，以得到比较客观的关联维数和最大Lyapunov指数。结果表明：采用关联维和最大Lyapunov指数作为判据，可以对悬架-车轮系统振动信号作周期性甄别，从而更准确地评价汽车悬架隔振性能。%The correctness for calculating the anti-vibration parameters of a suspension-wheel system depends on the quality of periodicity of the vibration signals of the system. But simply using the naked eyes can not effectively distinguish the periodicity differences of the vibration signals. In this article, the Grassberger-Procaccia (G-P) algorithm with small data sets is adopted to reasonably choose the embedding dimension, reconstruction delay, mean period of the time series and some other important parameters. And the scale-free zone is accurately delimited in the logarithmic-curve diagrams to get fairly objective correlation dimension and the maximum Lyapunov exponent. The results show that the correlation dimension and the maximum Lyapunov exponent can be used as criteria to distinguish the periodicity of the vibration signals for the suspension-wheel system, so that the vibration isolation performance of automobile’s suspensions can be evaluated more accurately.
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