Improved Naive Bayesian Classifier Method and the Application in Diesel Engine Valve Fault Diagnostic

Traditional diesel engine fault diagnostic technologies have increasingly shown deficiencies and shortcomings. Under this background, this paper adopts the naive Bayesian classifier method which built on the basis of the probability density function to diagnose the fault of diesel engine. Among all the improving approaches of Naive Bayesian classifier, integrated one-dependence estimators present their advantages both in accuracy and complexity. This paper proposes a new approach 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.