A Hybrid Model for on-line Detection of Gas Turbine Lean Blowout Events



Published Jul 2, 2018
Matteo Iannitelli Carmine Allegorico Francesco Garau Marco Capanni


Modern dry low NOx combustors can target very low emissions levels by operating at a lean air/gas ratio. However, ultra-lean combustion is extremely susceptible to thermoacoustic combustion instabilities and Lean Blowout (LBO), which can lead to large pressure oscillations in the combustor and decreased durability of components.

Conventional on-board diagnostics embedded in the Unit Control Panel (UCP) of a Gas Turbine, continuously check the health status of the combustion section at a high scan rate and raise alarms when abnormal conditions occur. While ensuring protection and control, UCP control logics may not provide precise indications on the nature of the issue and further troubleshooting, also using specific tools, is typically required.

In a changing environment where Industrial Internet of Things (IIoT) is offering a chance to drive productivity and growth, online Monitoring and Diagnostic (M&D) software and services on connected units are becoming strategic to increase asset availability and reliability, as well as reducing maintenance costs.

In this paper, we present a hybrid analytic, which combines physics-based and data-driven models, for the detection of Lean Blowout conditions on Gas Turbines equipped with Dry Low NOx multi-can combustion system. Regarding the data-driven model, we face a problem of classification and exploit dimensionality reduction to reduce the number of variables under consideration. During the development, different techniques are tested and benchmarked.

The analytic is trained on real LBO events and finally is deployed in a production environment to process incoming on-line data acquired from monitored units. Obtained results are presented.

How to Cite

Iannitelli, M., Allegorico, C., Garau, F., & Capanni, M. (2018). A Hybrid Model for on-line Detection of Gas Turbine Lean Blowout Events. PHM Society European Conference, 4(1). https://doi.org/10.36001/phme.2018.v4i1.405
Abstract 494 | PDF Downloads 606



diagnostic, gas turbine, blowout, classification, fault detection, machine learning, PCA, LDA, logistic regression, decision tree, combustion, hybrid model

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