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An intelligent sensor validation and fault diagnostic technique for diesel engines

Lookup NU author(s): Professor Ehsan Mesbahi

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Abstract

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.


Publication metadata

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


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