FTA-SVM-based fault recognition for vehicle engine

Author

Jin-Yong Zheng ; Zong-Xiao Yang ; Gang-Gang Wu ; Xi-Mei Li ; Jun Wang

Author_Institution

Henan Eng. Lab. of Wind Power Syst., Henan Univ. of Sci. & Technol., Luoyang, China

fYear

2015

fDate

9-11 April 2015

Firstpage

180

Lastpage

184

Abstract

Engine testing technology has made great development and gathering the engine data of failure becomes more and more easily. The engine fault recognition method based on data driving has made rapid development. The support vector machine (SVM) is currently a well-known machine learning technique. It has been applied to deal with range of fault recognition problems due to its unique advantages. There must to have two elements before use SVM: the one is a definite fault pattern, the other one is the mapping model of feature data and fault pattern for training SVM model. To tackle this problem, this paper raised a new method of engine fault recognition which combines the support vector machine (SVM) with fault tree analysis (FTA). Experimental results show that the method of FTA-SVM-based fault recognition is more effective and reasonable.

Keywords

diesel engines; fault diagnosis; fault trees; learning (artificial intelligence); mechanical engineering computing; support vector machines; FTA-SVM-based fault recognition; definite fault pattern; engine fault recognition method; fault pattern mapping model; fault tree analysis; feature data mapping model; machine learning technique; support vector machine; vehicle engine testing technology; Analytical models; Data models; Engines; Fault diagnosis; Fault trees; Support vector machines; Training; Data drive; Fault Tree Analysis; Fault recognition; Support Vector Machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Networking, Sensing and Control (ICNSC), 2015 IEEE 12th International Conference on

Conference_Location

Taipei

Type

conf

DOI

10.1109/ICNSC.2015.7116031

Filename

7116031