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Condition Monitoring of Combustion System on Industrial Gas Turbines Based on Trend and Noise Analysis

[+] Author Affiliations
Yu Zhang, Miguel Martínez-García, Samuel Cruz-Manzo

University of Lincoln, Lincoln, UK

Mike Garlick, Anthony Latimer

Siemens Industrial Turbomachinery Ltd., Lincoln, UK

Paper No. GT2017-64288, pp. V006T05A018; 8 pages
doi:10.1115/GT2017-64288
From:
  • ASME Turbo Expo 2017: Turbomachinery Technical Conference and Exposition
  • Volume 6: Ceramics; Controls, Diagnostics and Instrumentation; Education; Manufacturing Materials and Metallurgy
  • Charlotte, North Carolina, USA, June 26–30, 2017
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 978-0-7918-5091-6
  • Copyright © 2017 by Siemens AG

abstract

In this paper, a scheme of an ‘early warning’ system is developed for the combustion system of Industrial Gas Turbines (IGTs), which attains low computational workload and simple programming requirements, being therefore employable at an industrial level. The methodology includes trend analysis, which examines when the measurement shows different trends from the other measurements in the sensor group, and noise analysis, which examines when the measurement is displaying higher levels of noise compared to those of the other sensors. In this research, difficulties encountered by other data-driven methods due to temperature varying with load conditions of the IGT’s have also been overcome by the proposed approach. Furthermore, it brings other advantages, for instance, no historic training data is needed, and there is no requirement to set thresholds for each sensor in the system. The efficacy and effectiveness of the proposed approach has been demonstrated through experimental trials of previous pre-chamber burnout cases. And the resulting outcomes of the scheme will be of interest to IGT companies, especially in condition monitoring of the combustion system. Future work and possible improvements are also discussed at the end of the paper.

Copyright © 2017 by Siemens AG

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