Skip to content Skip to navigation
University of Warwick
  • Study
  • |
  • Research
  • |
  • Business
  • |
  • Alumni
  • |
  • News
  • |
  • About

University of Warwick
Publications service & WRAP

Highlight your research

  • WRAP
    • Home
    • Search WRAP
    • Browse by Warwick Author
    • Browse WRAP by Year
    • Browse WRAP by Subject
    • Browse WRAP by Department
    • Browse WRAP by Funder
    • Browse Theses by Department
  • Publications Service
    • Home
    • Search Publications Service
    • Browse by Warwick Author
    • Browse Publications service by Year
    • Browse Publications service by Subject
    • Browse Publications service by Department
    • Browse Publications service by Funder
  • Statistics
  • Help & Advice
University of Warwick

The Library

  • Login

Fault diagnosis application in an automotive diesel engine using auto-associative neural networks

Tools
- Tools
+ Tools

Antory, David (2006) Fault diagnosis application in an automotive diesel engine using auto-associative neural networks. In: International Conference on Computational Intelligence for Modelling, Control and Automation/International Conference on Intelligent Agents Web Technologies and International Commerce, Vienna, AUSTRIA, NOV 28-30, 2005. Published in: International Conference on Computational Intelligence for Modelling, Control & Automation Jointly with International Conference on Intelligent Agents, Web Technologies & Internet Commerce, Vol 2, Proceedings pp. 109-116.

Full text not available from this repository.

Abstract

The application of a new method for fault diagnosis in an automotive diesel engine is presented. Two common types of fault are investigated: (i) sensor fault, caused by a bias in the inlet manifold pressure sensor and (ii) process fault, caused by small air leaks in the inlet manifold plenum chamber. Such faults may lead to increased emission levels which, if left undetected, can eventually degrade engine performance. A diagnostic model using a variant of auto-associative neural networks (AANN), which has a unique architecture which shows great potential for fault diagnosis, is investigated. This new variant uses a reduced set of original data for the input-target network and is denoted as a generalized serial T2T (GST2T) network. The proposed GST2T model is experimentally validated using data from a real engine embedded in a chassis dynamometer at a test-cell facility. It is demonstrated that using just one diagnostic model, the different types of common faults mentioned above can be accurately detected and diagnosed. This new GST2T network constitutes a nonlinear extension of linear principal component analysis.

Item Type: Conference Item (UNSPECIFIED)
Subjects: Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
T Technology > TL Motor vehicles. Aeronautics. Astronautics
Journal or Publication Title: International Conference on Computational Intelligence for Modelling, Control & Automation Jointly with International Conference on Intelligent Agents, Web Technologies & Internet Commerce, Vol 2, Proceedings
Publisher: IEEE
ISBN: 0-7695-2504-0
Editor: Mohammadian, M
Date: 2006
Number of Pages: 8
Page Range: pp. 109-116
Publication Status: Published
Title of Event: International Conference on Computational Intelligence for Modelling, Control and Automation/International Conference on Intelligent Agents Web Technologies and International Commerce
Location of Event: Vienna, AUSTRIA
Date(s) of Event: NOV 28-30, 2005
URI: http://wrap.warwick.ac.uk/id/eprint/32688

Data sourced from Thomson Reuters' Web of Knowledge

Request changes to a record

Actions (login required)

View Item View Item
twitter

Email us: publications@warwick.ac.uk
Contact Details
About Us