Critical knock diagnosis for gasoline engines based on neural network with wavelet transform and fuzzy clustering

It is difficult to detect critical knock for a gasoline engine by the common method of knock diagnosis. In this paper, a new approach is presented to detect critical knock for gasoline engines. Based on this approach knock diagnosis consists of four steps. Firstly, discrete wavelet transform (DWT) is chosen as a pre-processor for a neural network to extract knock characteristic signals; Secondly, four characteristic factors are selected and calculated from knock characteristic signals; Thirdly, degree of memberships of the characteristic factors are calculated as the input and output of the neural network; and finally a RBF(Radial Basis Function) neural network is chosen, trained and applied to detect critical knock. Knock experiments were performed on a gasoline engine, and the application of the presented approach was studied. The results show that the presented method is practicable and can be applied to control the ignition of a gasoline engine working under critical knock which is admitted as an improved state of engine performance.