Fault Diagnosis of Automotive Engines Using Fuzzy Relevance Vector Machine

Wong, Pak Kin; Vong, Chi‐Man; Zhang, Zai Yong; Xu, Qingsong

doi:10.1007/978-3-642-24999-0_30

Cited by 2 publications

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Various fault diagnosis instruments should be combined with the experimental conclusion, personal experience to judge the fault, else the long-distance driver cannot eliminate the car failure without the guidance of experts. Therefore, fault diagnosis instrument can not be replaced using appropriate methods of analysis automobile fault diagnosis in the future for a long period of time [1]. Automobile fault system always expressed various fault phenomenon, such as fuel supply system common faults often can't start, for the power of the engine, the engine exhaust smoke color is not normal, the engine "coaster" etc.. Usually the fault reason and fault have no relationship: a fault may produce many kinds of failure, a failure may have a variety of reasons.…”

Section: Introductionmentioning

confidence: 99%

Automotive Fault Diagnosis Based on the Fuzzy Mathematics

Zhang

Gai²

2014

AMR

View full text Add to dashboard Cite

Automobile fault diagnosis is one of the hot issues in detection and diagnosis technology of automobile. The automobile fault has the characteristics of uncertainty and complexity problems. To solve the problems of qualitative description from drivers into quantitative, fuzzy mathematics is introduced. In addition, the fuzzy relationship of automobile fault and fault causes is expressed by setting the membership functions and fuzzy relation matrix. Finally, the qualitative analysis of real cases is present. The fuzzy theory and expert system for automobile fault diagnosis method is proved accurately through the algorithm analysis and real cases.

show abstract

Section: Introductionmentioning

confidence: 99%