Cart (Loading....) | Create Account
Close category search window
 

Detection and Diagnosis of Incipient Faults in Heavy-Duty Diesel Engines

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Morgan, I. ; Intell. Syst. & Robot. Group, Univ. of Portsmouth, Portsmouth, UK ; Honghai Liu ; Tormos, B. ; Sala, A.

This paper proposes a new methodology for detecting and diagnosing faults found in heavy-duty diesel engines based upon spectrometric analysis of lubrication samples and is compared against a conventional method, the redline limits, which is utilized in a number of major laboratories in the U.K. and across Europe. The proposed method applies computational power to a well-known maintenance technique and consists of an improved method of preprocessing to form a derivative tuple, which extracts further information from the measured elemental concentrations. To identify incipient faults, the distance in vector space is calculated using a Gaussian contour, generated from prior data, as the zero crossing, which enables novel samples to be classified as normal or abnormal. This information is utilized as the input to a probabilistic directed acyclic graph in the form of a belief network. This network provides a prognosis for the mechanism as well as suggesting possible actions that could be taken to rectify the diagnosed problem, supported with confidence probabilities. The proposed method is evaluated for both accuracy in detecting a fault as well as the duration of time that is provided before the event occurs, with significant improvements in both metrics demonstrated over the conventional method.

Published in:

Industrial Electronics, IEEE Transactions on  (Volume:57 ,  Issue: 10 )

Date of Publication:

Oct. 2010

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.