Methods of chaotic time series analysis are applied to typical engine data in order to identify and characterize deterministic effects. Pressure and engine-speed time series from three different spark-ignition internal combustion engines are examined as example data sets.
The mutual information function is evaluated comparatively with the autocorrelation function to measure temporal coupling in peak-pressure sequences and engine-speed time series. The utility of windowing or Poincaré sectioning the data in order to highlight portions of the engine cycle is developed and discussed. Additionally, estimates of Kolmogorov entropy and correlation dimension and the method of surrogate data suggest a discernible degree of nonlinear deterministic effects in the examined data.
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Updated: 1998-01-05 ceaf