Self-Organizing Maps for Virtual Sensors, Fault Detection and Fault Isolation in Diesel Engines

This project 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 project a feasible 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.
Source: Linköping University
Authors: Bergkvist, Conny | Wikner, Stefan

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