Paper Info
Title
Sensor fault detection and isolation in diesel air path using fuzzy-ARTMAP neural network
Abstract
Fault detection and isolation have become one of the most important aspects of automotive diagnosis. In this paper, a new approach is proposed dealing with fault detection and isolation problem in diesel engine. Especially, the sensors fault detection and isolation problem in diesel air path is studied. The proposed solution is realized in two stages. In the first one, we classify the unfaulty functioning data of system using the fuzzy-ARTMAP classification in order to model the engine dynamics. In the second stage, a conflict is evaluated between samples of test data based on the hyper-rectangles resulted in the first stage. Two samples are in conflict if their intersection does not belong to the neural model elaborated by fuzzy-ARTMAP. The model is learned and validated using data generated by xMOD software. This tool is also used for test. Finally, to illustrate our approach, some simulation results are given and discussed.
Year
DOI
Venue
2013
10.1109/ICNSC.2013.6548828
Networking, Sensing and Control
Keywords
Field
DocType
automotive engineering,diesel engines,fault diagnosis,mechanical engineering computing,neural nets,pattern classification,automotive diagnosis,data classification,diesel engine air path,engine dynamics,fuzzy-ARTMAP neural network,sensor fault detection,sensor fault isolation,xMOD software,Diesel engine air path,automotive diagnosis,fuzzy-ARTMAP,neural networks classification,sensor fault
Automotive engineering,Diesel fuel,Fault detection and isolation,Computer science,Fuzzy logic,Control engineering,Software,Test data,Artificial neural network,Diesel engine,Automotive industry
Conference
ISSN
ISBN
Citations 
1810-7869
978-1-4673-5199-7
0
PageRank 
References 
Authors
0.34
11
4
Name
Order
Citations
PageRank
Mohamed Guermouche132.16
Mourad Benkaci232.44
Ghaleb Hoblos3146.40
Nicolas Langlois42612.61