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Model-based Fault Detection for a Turbocharger

Zhang, H., Shi, John Z., Gu, Fengshou, Mishra, Rakesh and Ball, Andrew (2009) Model-based Fault Detection for a Turbocharger. In: Proceedings of COMADEM 2009. Condition Monitoring and Diagnostic Engineering Management (COMADEM). ISBN 9788493206468

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Abstract

Turbochargers are widely used on automotive, power generation and marine applications. However, while they are
being used, several types of faults such as rotor unbalance, journal bearings abrasion, insufficient oil supply, failure
from excessive exhaust temperatures, stable and stall, etc might happen. It would be a time and money consuming
work, even sometimes impossible to inspect periodically. In order to detect and classify those kinds of faults
effectively, this paper tries to apply a model-based approach on turbocharger monitoring. Firstly, a mathematic model
for a turbocharger is developed in Rotor-dynamics considering the effects of gyroscopic and journal bearing. Secondly,
the model of the whole system is validated by relevant experiments. Thirdly, data processing and faults classification
techniques are used to analyze the residual signals between those collected from faulting turbochargers and simulated
by the model. Finally, a conclusion will be drawn on the performance of this approach on the specific application.

Item Type: Book Chapter
Additional Information: Paper presented at COMADEM 2009, San Sebastian, Spain, 9 - 11 June 2009, Oral Session 9 Structural analysis Wednesday, 10th June, 2009
Subjects: T Technology > T Technology (General)
T Technology > TJ Mechanical engineering and machinery
Schools: School of Computing and Engineering
School of Computing and Engineering > Automotive Engineering Research Group
School of Computing and Engineering > Pedagogical Research Group
School of Computing and Engineering > Diagnostic Engineering Research Centre
School of Computing and Engineering > Diagnostic Engineering Research Centre > Energy, Emissions and the Environment Research Group
School of Computing and Engineering > Diagnostic Engineering Research Centre > Machinery Condition and Performance Monitoring Research Group
School of Computing and Engineering > Diagnostic Engineering Research Centre > Measurement System and Signal Processing Research Group
School of Computing and Engineering > Informatics Research Group
School of Computing and Engineering > Informatics Research Group > XML, Database and Information Retrieval Research Group
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Depositing User: Sara Taylor
Date Deposited: 19 May 2009 16:27
Last Modified: 14 Sep 2011 12:28
URI: http://eprints.hud.ac.uk/id/eprint/4409

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