Browsing by Author "Jombo, Gbanaibolou"

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    Diagnostics of gas turbine systems using gas path analysis and rotordynamic response approach
    (ISABE, 2017-09-08) Jombo, Gbanaibolou; Sampath, Suresh; Gray, Iain
    The modern gas turbine is plagued with issues centred on improving engine availability and limiting component degradation. The integrated use of different condition monitoring techniques presents a solution to addressing these challenges. This paper lays a foundation for the integration of gas path analysis and the rotordynamic response of the compressor to monitor the effect of fouling in the compressor. In investigating the resultant interaction between the aerodynamic and rotordynamic domain in a compressor caused by fouling, an approach involving the interaction of four different models is explored. The first model, a gas turbine engine performance model is used to simulate a fouled compressor and quantify the extent of performance deterioration with gas path analysis. The extent of performance deterioration from the engine performance model represented by scaling of the compressor maps becomes an input in the second model, a Moore-Greitzer compression system model, which evaluates the disturbed flow field parameters in the fouled compressor. The third model, a momentum-based aerodynamic force model, predicts the fouling induced aerodynamic force based on the disturbed flow field parameters. The aerodynamic force acting as a forcing function in the fourth model, a compressor rotordynamic model, produces the vibration response. From the investigation carried out in this work, it is observed, as the rate of fouling increases in the compressor, typified by a decrease in compressor massflow, pressure ratio and isentropic efficiency, there is a corresponding increase in the vibration amplitude at the first fundamental frequency of the compressor.
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    ItemOpen Access
    Improved gas turbine diagnostics towards an integrated prognostic approach wiht vibration and gas path analysis
    (2017-01) Jombo, Gbanaibolou; Sampath, Suresh; Pilidis, Pericles
    The degradation of a gas turbine engine in operation is inevitable, leading to losses in performance and eventually reduction in engine availability. Several methods like gas path analysis and vibration analysis have been developed to provide a means of identifying the onset of component degradation. Although both approaches have been applied individually with successes in identifying component faults; localizing complex faults and improving fault prediction confidence are some of the further benefits that can accrue from the integrated application of both techniques. Although, the link between gas path component faults and rotating mechanical component faults have been reported by several investigators, yet, gas path fault diagnostics and mechanical fault diagnostics are still treated as separated toolsets for gas turbine engine health monitoring. This research addresses this gap by laying a foundation for the integration of gas path analysis and vibration to monitor the effect of fouling in a gas turbine compressor. Previous work on the effect of compressor fouling on the gas turbine operation has been on estimating its impact on the gas turbine’s performance in terms of reduction in thermal efficiency and output power. Another methodology often used involves the determination of correlations to characterize the susceptibility and sensitivity of the gas turbine compressor to fouling. Although the above mentioned approaches are useful in determining the impact of compressor fouling on the gas turbine performance, they are limited in the sense that they are not capable of being used to access the interaction between the aerodynamic and rotordynamic domain in a fouled gas turbine compressor. In this work, a Greitzer-type compression system model is applied to predict the flow field dynamics of the fouled compressor. The Moore-Greitzer model is a lumped parameter model of a compressor operating between an inlet and exit ii duct which discharges to a plenum with a throttle to control the flow through the compression system. In a nutshell, the overall methodology applied in this work involves the interaction of four different models, which are: Moore-Greitzer compression system model, Al-Nahwi aerodynamic force model, 2D transfer matrix rotordynamic model and a gas turbine performance engine model. The study carried out in this work shows that as the rate of fouling increases, typified by a decrease in compressor massflow, isentropic efficiency and pressure ratio, there is a corresponding increase in the vibration amplitude at the compressor rotor first fundamental frequency. Also demonstrated in this work, is the application of a Moore-Greitzer type compressor model for the prediction of the inception of unstable operation in a compressor due to fouling. In modelling the interaction between the aerodynamic and rotordynamic domain in a fouled gas turbine compressor, linear simplifications have been adopted in the compression system model. A single term Fourier series has been used to approximate the resulting disturbed flow coefficient. This approximation is reasonable for weakly nonlinear systems such as compressor operating with either an incompressible inlet flow or low Mach number compressible inlet flow. To truly account for nonlinearity in the model, further recommendation for improvement includes using a second order or two-term Fourier series to approximate the disturbed flow coefficient. Further recommendation from this work include an extension of the rotordynamic analysis to include non-synchronous response of the rotor to an aerodynamic excitation and the application of the Greitzer type model for the prediction of the flow and pressure rise coefficient at the inlet of the compressor when fouled.
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    ItemOpen Access
    Integrated gas turbine system diagnostics: components and sensor faults quantification using artificial neural networks
    (International Society for Air Breathing Engines (ISABE), 2017-09-11) Osigwe, Emmanuel O.; Li, Yi-Guang; Suresh, Sampath; Jombo, Gbanaibolou
    The role of diagnostic systems in gas turbine operations has changed over the past years from a single support troubleshooting maintenance to a more proactive integrated diagnostic system. This has become so, because detecting and fixing fault(s) on one gas turbine sub-system can trigger false fault(s) indication, on other component(s) of the gas turbine system, due to interrelationships between data obtained to monitor not only the GT single component, but also the integrated components and sensors. Hence, there is need for integration of gas turbine system diagnostics. The purpose of this paper is to present artificial neural network diagnostic system (ANNDS) as an integrated gas turbine system diagnostic tool capable of quantifying gas turbine component and sensor fault. A model based approach which consists of an engine model, and an associated parameter estimation algorithm that predicts the difference between the real engine data and the estimated output data is described in this paper. The ANNDS system was trained to detect, isolate and assess component(s) and sensor fault(s) of a single spool industrial gas turbine GT-PG9171ER. The ANN model was construed with multi-layer feed-forward back propagation network for component fault(s) and auto associative network for sensor fault(s). The diagnostic methodology adopted was a nested network structure, trained to handle specific objective function of detecting, isolating or quantifying faults. The data used for training, and testing purposes were obtained from a non-linear aero-thermodynamic model using PYTHIA; a Cranfield University in-house software. The data set analyzed in this paper represent samples of clean and faulty gas turbine components caused by fouling (0.5% - 6% degradation) and sensor fault(s) due to bias (±1% - ±7%). The results show the capability of ANN to detect, isolate (classification) and quantify multiple faults if properly trained.