Browse by author
Lookup NU author(s): Professor Ehsan Mesbahi
Full text for this publication is not currently held within this repository. Alternative links are provided below where available.
An intelligent sensor validation and on-line fault diagnosis technique for a 6 cylinder turbocharged diesel engine is proposed and studied. A single auto-associative 3-layer Artificial Neural Network (ANN), is trained to examine the accuracy of the measured data and allocate a confidence level to each signal. The same ANN is used to recover the missing or faulty data with a close approximation. For on-line fault detection a feed-forward ANN is trained to classify and consequently recognize faulty and healthy behavior of the engine for a wide range of operating conditions. The proposed technique is also equipped with an on-line learning mechanism, which is activated when the confidence level in predicted fault is poor. It is hoped that a feasible, practical, and reliable sensor reading, as well as highly accurate fault diagnosis system, would be achieved. © 2001 by ASME.
Author(s): Mesbahi E
Publication type: Article
Publication status: Published
Journal: Journal of Dynamic Systems, Measurement and Control
Year: 2001
Volume: 123
Issue: 1
Pages: 141-144
ISSN (print): 0022-0434
ISSN (electronic): 1528-9028
Publisher: A S M E International
URL: http://dx.doi.org/10.1115/1.1343461
DOI: 10.1115/1.1343461
Altmetrics provided by Altmetric
