UM  > 科技學院  > 電腦及資訊科學系
Fault diagnosis of automotive engines using fuzzy relevance vector machine
Wong P.-K.; Vong C.-M.; Zhang Z.; Xu Q.
2011-11-11
Conference Name2nd International Conference on Theoretical and Mathematical Foundations of Computer Science (ICTMF 2011)
Source PublicationCommunications in Computer and Information Science
Volume164 CCIS
Pages213-220
Conference DateMAY, 2011
Conference PlaceSingapore, MALAYSIA
Abstract

For any faults of automotive engines, the diagnosis can be performed based on variety of symptoms. Traditionally, the description of the faulty symptom is just existence or not. However, this description cannot lead to a high accuracy because the symptom sometimes appears in different degrees. Therefore, a knowledge representation method which could precisely reflect the degree of the symptom is necessary. In this paper, the fuzzy logic is firstly applied to quantify the degrees of symptoms. A probabilistic classification system is then constructed by using the fuzzified symptoms and a new technique namely Fuzzy Relevance Vector Machine (FRVM). Moreover, both Fuzzy Probabilistic Neural Network (FPNN) and Fuzzy Probabilistic Support Vector Machine (FPSVM) are used to respectively construct similar classification systems for comparison with FRVM. Experimental results show that FRVM produces higher diagnosis performance than FPNN and FPSVM. © 2011 Springer-Verlag.

KeywordEngine Fault Diagnosis Fuzzy Probabilistic Neural Network Fuzzy Probabilistic Support Vector Machine Fuzzy Relevance Vector Machine
DOI10.1007/978-3-642-24999-0_30
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Theory & Methods
WOS IDWOS:000302377600030
Fulltext Access
Citation statistics
Document TypeConference paper
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
DEPARTMENT OF ELECTROMECHANICAL ENGINEERING
AffiliationUniversidade de Macau
Recommended Citation
GB/T 7714
Wong P.-K.,Vong C.-M.,Zhang Z.,et al. Fault diagnosis of automotive engines using fuzzy relevance vector machine[C],2011:213-220.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Wong P.-K.]'s Articles
[Vong C.-M.]'s Articles
[Zhang Z.]'s Articles
Baidu academic
Similar articles in Baidu academic
[Wong P.-K.]'s Articles
[Vong C.-M.]'s Articles
[Zhang Z.]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Wong P.-K.]'s Articles
[Vong C.-M.]'s Articles
[Zhang Z.]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.