Wang Xin

Work place: College of Marine Engineering, Dalian Maritime University, Dalian, China

E-mail: bfddwx@163.com

Website:

Research Interests: Information-Theoretic Security, Information Security, Artificial Intelligence

Biography

Wang   Xin,(1981-),  Male,   PHD Student,  Engaged  in  the  research  of intelligent fault diagnosis technique.

October 10, 1981 Xin was born in Dalian City, Liaoning Province. Who graduated from Dalian  University  in  2004,  Computer  Science  and Technology  specialized  field  and  with  a  Bachelor degree.  From  2005  to  2007,  Xin  studied  in  Dalian University  of  Technology  and  received  a  master's degree    of   Computer,    Who    became    a    marine engineering PHD    student.    in    Dalian    Maritime University  at  September  2008  and  Engaged  in  the research of intelligent fault diagnosis technique. Xin's papers includes:   ①Wang Xin,Yu Hongliang, Zhang Lin et al. Improved Genetic Algorithm Neural Network Method and the Application in Valve Fault Diagnosis of  Diesel  Engine.[C]  IEEE  The  2010  International Conference  on  Information   Security  and  Artificial Intelligence.  ②Zhang Lin, Wang Xin, Yu Hongliang, Research and Application on Improved Naive Bayesian Classifier Method,IEEE ICIECS2010。③Wang Xin,Yu Hongliang, Zhang Lin et al, Improved Naive Bayesian Classifier Method and the Application in Diesel Engine Valve Fault Diagnostic IEEE ICMTMA

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

By Wang Xin Yu Hongliang Zhang Lin Huang Chaoming Song Yuchao

DOI: https://doi.org/10.5815/ijigsp.2011.01.02, Pub. Date: 8 Feb. 2011

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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