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
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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