A Real-time Fault Detection Method for the Air-fuel Ratio of Gasoline Engine Based on the Trend and Correlation Analysis of Dataflow

Wu, S; Tang, Z; Chen, C

A Real-time Fault Detection Method for the Air-fuel Ratio of Gasoline Engine Based on the Trend and Correlation Analysis of Dataflow

Wu, S Tang, Z Chen, C

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Publication Type:: Journal Article
Citation:: Qiche Gongcheng/Automotive Engineering, 2017, 39 (4)
Issue Date:: 2017-04-25

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Field	Value	Language
dc.contributor.author	Wu, S	en_US
dc.contributor.author	Tang, Z	en_US
dc.contributor.author	Chen, C	en_US
dc.date.issued	2017-04-25	en_US
dc.identifier.citation	Qiche Gongcheng/Automotive Engineering, 2017, 39 (4)	en_US
dc.identifier.issn	1000-680X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/126854
dc.description.abstract	© 2017, Society of Automotive Engineers of China. All right reserved. According to the features of air fuel ratio fault in gasoline engine, a real-time fault detection method based on the abnormality of data flow trend and correlation analysis is proposed in this paper. For enhancing accuracy and operation efficiency, modifications are made based on the algorithm of cumulative sum of error, with rapid estimation on the correlation between data flows, to enable the whole system operates on on-board platform relatively sensitive to resources. The results of test on the engine of Chang-an vehicle show that compared with traditional fault detection method based on support vector machine and neural network, the method proposed can obtain better results of fault detection with lower resource consumption.	en_US
dc.relation.ispartof	Qiche Gongcheng/Automotive Engineering	en_US
dc.relation.isbasedon	10.19562/j.chinasae.qcgc.2017.04.015	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	A Real-time Fault Detection Method for the Air-fuel Ratio of Gasoline Engine Based on the Trend and Correlation Analysis of Dataflow	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	39	en_US
utslib.for	0902 Automotive Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	39	en_US

Abstract:

© 2017, Society of Automotive Engineers of China. All right reserved. According to the features of air fuel ratio fault in gasoline engine, a real-time fault detection method based on the abnormality of data flow trend and correlation analysis is proposed in this paper. For enhancing accuracy and operation efficiency, modifications are made based on the algorithm of cumulative sum of error, with rapid estimation on the correlation between data flows, to enable the whole system operates on on-board platform relatively sensitive to resources. The results of test on the engine of Chang-an vehicle show that compared with traditional fault detection method based on support vector machine and neural network, the method proposed can obtain better results of fault detection with lower resource consumption.

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http://hdl.handle.net/10453/126854