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Study on Diesel Engine Fault Diagnosis Method based on Integration Super Parent One Dependence Estimator

Author(s): Wang Xin | Yu Hongliang | Zhang Lin | Huang Chaoming | Song Yuchao

Journal: International Journal of Image, Graphics and Signal Processing
ISSN 2074-9074

Volume: 3;
Issue: 1;
Start page: 10;
Date: 2011;
Original page

Keywords: diesel engine | naïve Bayesian classifier | fault diagnosis | one-dependence classifier

Under the background of the deficiencies and shortcomings in traditional diesel engine fault diagnostic, the naïve Bayesian classifier method which built on the basis of the probability density function is adopted to diagnose the fault of diesel engine. A new approach is proposed to weight the super-parent one dependence estimators. To verify the validity of the proposed method, the experiments are performed using 16 datasets collected by University of California Irvine (UCI) and 5 diesel engine datasets collected by our lab. The comparison experimental results with other algorithms demonstrate the effectiveness of the proposed method.
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