Condition monitoring of combustion system on industrial gas turbines based on trend and noise analysis

Zhang, Yu and Martinez, Miguel and Garlick, Mike and Latimer, Anthony and Cruz-Manzo, Samuel (2017) Condition monitoring of combustion system on industrial gas turbines based on trend and noise analysis. In: ASME TURBO EXPO 2017: Turbomachinery Technical Conference & Exposition, 26-30 June, 2017, Charlotte, NC USA.

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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 correlated 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.

Keywords:Industrial gas turbine, Combustion system, Condition monitoring, Trend analysis, Noise analysis
Subjects:G Mathematical and Computer Sciences > G700 Artificial Intelligence
G Mathematical and Computer Sciences > G760 Machine Learning
H Engineering > H321 Turbine Technology
Divisions:College of Science > School of Engineering
ID Code:27881
Deposited On:18 Jul 2017 14:51

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