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Self-organizing maps for virtual sensors, fault detection and fault isolation in diesel engines

This master thesis report discusses the use of self-organizing maps in a diesel engine management system. Self-organizing maps are one type of artificial neural networks that are good at visualizing data and solving classification problems. The system studied is the Vindax(R) development system from Axeon Ltd. By rewriting the problem formulation also function estimation and conditioning problems can be solved apart from classification problems. In this report a feasibility study of the Vindax(R) development system is performed and for implementation the inlet air system is diagnosed and the engine torque is estimated. The results indicate that self-organizing maps can be used in future diagnosis functions as well as virtual sensors when physical models are hard to accomplish.

Identiferoai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-2757
Date January 2005
CreatorsBergkvist, Conny, Wikner, Stefan
PublisherLinköpings universitet, Institutionen för systemteknik, Linköpings universitet, Institutionen för systemteknik, Institutionen för systemteknik
Source SetsDiVA Archive at Upsalla University
LanguageEnglish
Detected LanguageEnglish
TypeStudent thesis, info:eu-repo/semantics/bachelorThesis, text
Formatapplication/pdf
Rightsinfo:eu-repo/semantics/openAccess

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