Statistical engine misfire detection

Abstract

New recursive filtering algorithms for misfire detection based on the trigonometric interpolation method are proposed for spark ignition automotive engines. The technique improves the performance of the filtering algorithms, allowing a flexible choice of the size of the moving window. Correction algorithms are introduced for the recursive trigonometric interpolation method that ensure robustness with respect to round-off errors which are present in the finite precision implementation environment. New real-time statistical algorithms based on a hypothesis testing for a misfire detection are proposed. The statistical decision-making mechanism makes it possible to achieve misfire detection at a certain significance level with an automatically selected sample size depending on the signal quality, which in turn improves the robustness of the misfire detection algorithm. This work was done within the Volvo Six Sigma program.