Wang Xin; Yu Hongliang; Zhang Lin; Huang Chaoming; Song Yuchao

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

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Author(s)

Wang Xin ^1,* Yu Hongliang ¹ Zhang Lin ² Huang Chaoming ¹ Song Yuchao ¹

1. College of Marine Engineering, Dalian Maritime University, Dalian, China

2. School of Textile and Light Industry, Dalian Polytechnic University, Dalian, China

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2011.01.02

Received: 3 Nov. 2010 / Revised: 26 Nov. 2010 / Accepted: 6 Jan. 2011 / Published: 8 Feb. 2011

Index Terms

Diesel engine, naïve Bayesian classifier, fault diagnosis, one-dependence classifier

Abstract

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.

Cite This Paper

Wang Xin,Yu Hongliang,Zhang Lin,Huang Chaoming,Song Yuchao, "Study on Diesel Engine Fault Diagnosis Method based on Integration Super Parent One Dependence Estimator", IJIGSP, vol.3, no.1, pp.10-16, 2011. DOI: 10.5815/ijigsp.2011.01.02

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