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Fuzzy Model-Based Condition Monitoring of a Dry Vacuum Pump via Time and Frequency Analysis of the Exhaust Pressure Signal

Twiddle, J.A., Jones, N.B., Spurgeon, Sarah K. (2008) Fuzzy Model-Based Condition Monitoring of a Dry Vacuum Pump via Time and Frequency Analysis of the Exhaust Pressure Signal. Proceedings of the Institution of Mechanical Engineers Part C - Journal of Mechanical Engineering Science, 222 (2). pp. 287-293. ISSN 0954-4062. (doi:10.1243/09544062JMES651) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:17877)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL:
http://dx.doi.org/10.1243/09544062JMES651

Abstract

A fuzzy model-based diagnostic scheme is designed to monitor dry vacuum pump performance and detect two fault conditions, mechanical inefficiency and exhaust system blockage. The diagnostic scheme is based on time and frequency analysis of the exhaust pressure signal. Power ratios of certain frequency components in the signal spectrum can be used to predict the gas load, motor current and hence mechanical efficiency. Changes in the periodic features of the signal, symptomatic of fault conditions can be detected using a fuzzy reference model. A fuzzy rule base is used to analyse outputs of the reference model and the load estimator and produce a diagnosis of the pump condition. Experimental results show that the motor current estimation had a root mean squared error of 0.11 A (similar to 5 per cent). Two fault symptoms, a 29 per cent obstruction of the exhaust silencer and an 8 per cent increase in current with respect to gas load, were simulated on the pump test bed and successfully diagnosed.

Item Type: Article
DOI/Identification number: 10.1243/09544062JMES651
Uncontrolled keywords: dry vacuum pumps; condition monitoring; fault diagnosis; fuzzy logic
Subjects: T Technology > TJ Mechanical engineering and machinery > Control engineering
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: J. Harries
Date Deposited: 20 Apr 2009 14:07 UTC
Last Modified: 16 Nov 2021 09:56 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/17877 (The current URI for this page, for reference purposes)

University of Kent Author Information

Spurgeon, Sarah K..

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