Abstract
Abstract: The aim of this paper is to scrutinise closely information that contains in the acoustic emission of a gas-fuelled engine and to define the potential of knock condition detection. The recently introduced technique of nonstationary system identification is investigated. At first, it utilises the wavelet transform to reveal time-frequency energy density of data. Then the modified version of singular value decomposition is applied to extract dominant frequency components from the data buried in background noise. The efficacy of knock feature extraction is investigated using three sources of data: in-cylinder pressure, engine structure vibration and engine noise sensed by a microphone.
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