With the complexity increase of gasoline vehicle aftertreatment system, the problems such as large modeling difficulty and high calibration cost should be solved during the fault diagnosis process of threeway catalytic converter. Based on the advantages of neural network in dealing with nonlinear problems, a neural network based aging diagnosis algorithm for threeway catalytic converter of gasoline vehicle was proposed. According to the aging mechanism of threeway catalytic converter, the oxygen sensor signals before and after the catalyst were collected as feature inputs and the data set required for network training and testing was determined combined with different fault codes. Back propagation neural network(BPNN) and deep belief network(DBN) were used separately to optimize the parameters of training process and test the diagnostic results. The experimental results show that the neural networkbased diagnosis algorithm is simple in modeling and has high diagnostic accuracy with good generalization capability. From the perspective of diagnostic framework, DBN simplifies the feature extraction process and has higher diagnostic accuracy than BPNN.
LIU Yang,PAN Jinchong,ZHANG Yunlong,SHUAI Shijin.
Aging Diagnosis on Three-Way Catalytic Converter of Gasoline Vehicle Based on Neural Network[J]. Vehicle Engine. 2019, 0(1): 34-40 https://doi.org/10.3969/j.issn.1001-2222.2019.01.006
