Critical Knock Diagnosis for Gasoline Engines Based on Neural Network with Wavelet Transform and Fuzzy Clustering

Jian Guo Yang; Yan Yan Wang; Bo Lin

doi:10.4028/www.scientific.net/AMR.455-456.1084

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 455-456Critical Knock Diagnosis for Gasoline Engines...

Critical Knock Diagnosis for Gasoline Engines Based on Neural Network with Wavelet Transform and Fuzzy Clustering

Abstract:

. 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.

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Periodical:

Advanced Materials Research (Volumes 455-456)

Pages:

1084-1089

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.455-456.1084

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Online since:

January 2012

Authors:

Jian Guo Yang, Yan Yan Wang, Bo Lin

Keywords:

Critical Knock, Fuzzy Clustering, Gasoline Engine, Neural Network (NN), Wavelet Transform (WT)

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