A Data-Driven Approach for on-line Gas Turbine Combustion Monitoring using Classification Models

Carmine Allegorico and Valerio Mantini
Submission Type: 
Full Paper
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phmce_14_003.pdf691.17 KBJune 25, 2014 - 8:58am

Given the critical nature of Gas Turbines in most industrial plants, it is a high priority to find ways of reducing maintenance costs and increasing the availability. Quickly detecting and identifying combustion anomalies enables the choice of an appropriate recovery strategy, potentially mitigating the consequences of unscheduled down time and increased maintenance costs.
Monitoring the Exhaust Gas Temperature (EGT) profiles is a good means of detecting combustion problems: plugged nozzles and/or combustor and transition piece failures will always result in distorted exhaust gas temperature patterns. However the conventional monitoring systems do not allow robust discrimination between instrumental failures and real gas turbine issues; furthermore weak diagnostic methods can be source of numerous false alarms.
In this paper, we investigate the problem of monitoring the combustion chambers of a gas turbine and we attempt to address this issue by introducing a strategy for automatic and efficient patterns recognition by using Machine Learning Classification algorithms.
Some historical events have been firstly retrieved and analyzed to discover which features are useful for classification. Based on the observations, two multiclass classification algorithms, one based on logistic regression, the other on Artificial Neural Networks (ANN), have been developed. Finally, real-world datasets have been used to benchmark the performance of the proposed algorithms against a traditional physics-based approach.

Publication Year: 
2014
Publication Volume: 
5
Publication Control Number: 
003
Page Count: 
9
Submission Keywords: 
classification
Automatic diagnostics
gas turbine
oil and gas
Data-driven detection methodologies
data driven methods
Exhaust Gas Temperature
Artificial Neural Networks (ANN)
combustion
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
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