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In-cylinder pressure traces vary significantly from cycle-to-cycle in spark-ignition (SI) engines. The variations, substantially present even when engine is stable, are magnified under certain engine operating conditions. As a result, engine torque output oscillates and engine operation becomes unstable. EGR tolerance, lean burn limit and spark retard capabilities at CSSRE (Cold Start Spark Retard and Enleanment) are mostly determined by the levels of cycle-to-cycle variations. None of the engine computer models, however, have included cyclic variations for routine industrial applications. As the application domain of engine simulation models expands into unstable engine operating conditions, the modeling of cyclic variations becomes increasingly important. In this research, reviews were conducted regarding different approaches for the simulation of cyclic variation.

This paper describes a fuzzy model that is designed to diagnose automotive engineering faults. The fuzzy model has two modes, L-mode, which is the fuzzy learning mode and T-mode, which is the test mode. In the L-mode, the system learns two types of engineering diagnostic knowledge, expert knowledge, and the knowledge acquired from training data using machine learning techniques. A fuzzy diagnostic system for engine vacuum leak detection has been implemented based on this fuzzy model. The system has been tested on the data downloaded directly from the test sites of assembly plants of the Ford Motor Company, and its performance is excellent.