
Knocking Detection in Gasoline Engines Based on Probability Density Functions: A Mixed Gaussian Distribution ApproachKnocking Detection in Gasoline Engines Based on Probability Density Functions: A Mixed Gaussian Distribution Approach Knocking Detection in Gasoline Engines Based on Probability Density Functions: A Mixed Gaussian Distribution Approach 
"/伊吹, 竜也/"伊吹, 竜也 ,
"/Ibuki, Tatsuya/"Ibuki, Tatsuya ,
"/粟井, 康裕/"粟井, 康裕 ,
"/awai, yasuhiro/"awai, yasuhiro ,
"/坂柳, 佳宏/"坂柳, 佳宏 ,
"/SAKAYANAGI, YOSHIHIRO/"SAKAYANAGI, YOSHIHIRO ,
"/三平, 満司/"三平, 満司 ,
"/SAMPEI, MITSUJI/"SAMPEI, MITSUJI
pp.191

196 , 201512 , IEEE
Description
This paper proposes an engine knocking detection approach based on probability density functions (PDFs). In this work, we suppose that the PDF of knocking intensity distribution can be approximated by a mixture of two Gaussianfunctions due to normal combustion and abnormal one. We first apply EM (ExpectationMaximization) algorithm to actual engine data to show that the knocking intensity probability distribution can be successfully estimated by the mixed Gaussian distribution. We next try to apply the existing online EM algorithm in view of the actual implementation by an engine control unit. However, since the existing algorithm is built for the estimation of a fixed PDF, the effect of new input data is gradually attenuated and vanishes after long time. We thus newly propose a modified online EM algorithm so that the effect of the new input data is not attenuated. Finally, we perform simulations and an actual engine bench test for the validity of the present approach.