| Summary: | Piston engines fueled by kerosene have a strong application prospect in special vehicles and small aircrafts, but the low amount of octane in kerosene fuel causes its knock combustion phenomenon to be particularly serious. A knock will deteriorate the power and economy of the engine and will damage the engine in serious cases. Therefore, knocking is the key problem with kerosene engines. We propose a knock-prediction system for kerosene engines based on in-cylinder pressure signals. Firstly, the intrinsic mode function (IMF) caused by knock resonance is extracted from the in-cylinder pressure signal via empirical mode decomposition (EMD) and a time–frequency domain analysis. A time-domain statistical analysis (TDSA) combined with a principal component analysis (PCA) is used to extract features from the IMF. Finally, the data collected from the test bench are trained by a support vector machine to obtain the knock-prediction model. Compared with other technical combinations for training, the proposed scheme achieved more accurate results in knock prediction. Considering the working characteristics of kerosene engines, a slight knock can increase the power of a kerosene engine. Therefore, some incorrectly predicted cycles (slight-knock cycles) do not affect the normal operation of the engine.
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