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Noise source identification and separation of internal combustion engines is an effective tool for engine NVH (noise, vibration and harshness) development. Among various experimental approaches, noise source identification using signal processing has attracted extensive attention because of that the signal can be easily acquired and the requirements for equipment is relatively low. In this paper, variational mode decomposition (VMD) combined with independent component analysis (ICA) is used for noise source identification of a turbo-charged gasoline engine. Existing algorithms have been proved to be effective to extract signal features but also have disadvantages. One of the key problems in presently used method is that the main components of the signal, i.e. the main source of the noise, are unknown in advance. Thus the parameters selection of signal processing algorithms, which has a significance influence on the identification result, has no uniform criterion.

This paper reports an investigation of knock detection in spark ignition (SI) engines using EEMD-Hilbert transform based on the engine cylinder block vibration signals. Ensemble Empirical Mode Decomposition (EEMD) was used to de-compose the signal and detect knock characteristic. Hilbert transform was used to analysis the frequency information of knock characteristics. The result shows that for cylinder block vibration signals, the EEMD algorithm could extract the knock characteristic (include light knock), and the Hilbert transform result shows that the instantaneous of knock characteristics concentrate in 5000-10000Hz. At last, the knock window is then determined, based on which a new knock intensity evaluation factor K is proposed, and the results show that, the parameter K is reasonable and effective.