DEVELOPMENT OF A NEW MISFIRE DETECTION SYSTEM USING NEURAL NETWORK

The detection of engine misfire events is one of major concerns in engine control due to its negative effect on air pollution and engine performance. In this paper, a misfire detection system based on crankshaft angular speed fluctuation is developed. Synthetic variable method is adopted for the preprocessing of crankshaft angular speed. This method successfully estimates the work output of each cylinder by finding the effect of combustion energy on the crankshaft rotational speed or acceleration after virtually removing the effect of the internal inertia forces from the measured crankshaft speed signals. The detection system is developed using neural network with the revised synthetic angular acceleration as input which is derived from the preprocessing. Mathematical simulation is carried out for developing and verifying the misfire detection system. Finally, the reliability of the developed system is validated through an experiment.