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Intelligent torque estimation and fault diagnosis for an IC race engine


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Polson, Cranston Phillip 2002 , 'Intelligent torque estimation and fault diagnosis for an IC race engine', Research Master thesis, University of Tasmania.

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There is a remarkable improvement in the engine design and its sensory control over
the last three-four decades in automotive industry. Several pressing issues such as
reduced Hydro Carbons, improved fuel efficiency and optimum power for efficient
engine performance are continually investigated in modem automobile companies.
While the technological 'know-how' is reaching optimum in terms of the automobile
body design, aerodynamics and comfort aspects, there is sufficient room for
development for optimum engine performance.
There is evidence that the modem automotive companies are adopting certain modem
control strategies and emerging technologies such as fuzzy logic and evolutionary
algorithms for better engine performance. The IC (Internal Combustion) engine
performance and fault recognition is a major research issue. It is very well know that
there can be more than one cause that contributes to the same effect in an IC engine.
This aspect puts more pressure on the need for reliable quantitative models for fault
diagnosis to identify specific cause of the same effect. On the other hand a reliable
control of the air-mass flow and the air-fuel ratio plays a significant role in controlling
both power and Hydro Carbon emissions into the atmosphere.
Automotive companies are continually attempting to model IC engines for fault
diagnosis and performance prediction using traditional modelling techniques such as
heat transfer models and empirical investigation using knowledge base. An online
estimation of torque and power, in absence of chassis dynamometers, in dynamic
conditions is also an important aspect to find out the functional behaviour at any given
time for the engine.
In this thesis intelligent neural network models are proposed for prediction of torque,
power and air-mass flow for a 600cc-race engine. Using extensive experimental
investigation, it is shown that, the neural network architectures predict power, torque
and the air-mass flow to an excellent accuracy for eventual on-line control over a
range of engine operating conditions.

Item Type: Thesis - Research Master
Authors/Creators:Polson, Cranston Phillip
Keywords: Internal combustion engines, Intelligent control systems, Neural networks (Computer science), Multisensor data fusion, Automotive sensors, Motor vehicles
Copyright Holders: The Author
Copyright Information:

Copyright 2002 the author - The University is continuing to endeavour to trace the copyright
owner(s) and in the meantime this item has been reproduced here in good faith. We
would be pleased to hear from the copyright owner(s).

Additional Information:

No access until 27 September 2007. Thesis (M. Eng. Sc.) )--University of Tasmania, 2003. Includes bibliographical references

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