DocumentCode
3363901
Title
Adaptive Neural Fuzzy Networks Model of Automobile Performance Monitoring
Author
Lifang, Kong ; Dong, Li ; Ying, Zhao
Author_Institution
Air Force Logistic Acad., Xuzhou, China
Volume
2
fYear
2012
fDate
26-27 Aug. 2012
Firstpage
72
Lastpage
75
Abstract
The model for automobile engine performance monitoring and fault detection was proposed based on adaptive neural fuzzy interference system. With recognition mechanism of the adaptive neural fuzzy interference system, according to the properties of entropy, this paper using entropy optimizes the input interface of adaptive neural fuzzy interference system , this model was combined with characteristic performance of automobile engine to attain the degrees of engine performance´s abnormal state for monitoring engine performance. The approach can sensitively and accurately reflect the whole performance of the engine. Meanwhile, this method improves the rate of identifying whether the performance of the engine is normal or not, finds out the potential forepart fault of engine and prevents the spread of the fault. The validity of this method is testified by monitoring certain type of cummins engine 6BT5.9.
Keywords
automotive engineering; fuzzy neural nets; internal combustion engines; mechanical engineering computing; adaptive neural fuzzy interference system; adaptive neural fuzzy network model; automobile engine performance monitoring; automobile performance monitoring; entropy; fault detection; input interface; recognition mechanism; Adaptation models; Adaptive systems; Engines; Entropy; Indexes; Interference; Monitoring; Adaptive neural fuzzy interference system; Auto-engine; Fault detection; Performance monitoring;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2012 4th International Conference on
Conference_Location
Nanchang, Jiangxi
Print_ISBN
978-1-4673-1902-7
Type
conf
DOI
10.1109/IHMSC.2012.113
Filename
6305727
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