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
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;
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
DOI :
10.1109/IHMSC.2012.113