Title :
The Vibration Parameter Fault Diagnosis Cloud Model for Automobile Engine Based on ANFIS
Author :
Kong, Li-Fang ; Shi, Rong-Ling ; Tian Zhang ; Hao Wei
Author_Institution :
Basic Depts., Xuzhou Air Force Coll., Xuzhou, China
Abstract :
In order to solve the fault diagnosis problem of Vibration Parameter, Adaptive Neuro-Fuzzy inference system (ANFIS) was applied to build a fault diagnosis model of automobile engine and induce cloud model of fan-out, outputting results are continued. Through verification of the built diagnosis model with data of engine tests, it has been found that the recognition accuracy increase from 88.75% to 99.68%, training error falling from 0.001683 to 0.0011526. Simulation results show that the fitting ability, convergence speed and recognition accuracy of improved ANFIS model are all superior to ANFIS. So a contingent fault of automobile engine can be identified effectively.
Keywords :
automotive engineering; condition monitoring; fault diagnosis; inference mechanisms; internal combustion engines; mechanical engineering computing; vibrations; ANFIS; adaptive neuro fuzzy inference system; automobile engine; fault diagnosis cloud model; vibration parameter; Adaptation model; Automobiles; Clouds; Data models; Engines; Fault diagnosis; Training;
Conference_Titel :
Computational Intelligence and Software Engineering (CiSE), 2010 International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-5391-7
Electronic_ISBN :
978-1-4244-5392-4
DOI :
10.1109/CISE.2010.5677006