Development of a real time intelligent health monitoring platform for aero-engine

Giorgi, Maria Grazia De; Campilongo, Stefano; Ficarella, Antonio

doi:10.1051/matecconf/201823300007

Cited by 14 publications

(6 citation statements)

References 7 publications

(9 reference statements)

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“…For example, a transient model of the Viper 632-43 turbojet was implemented in our earlier project [52]. An integrated health monitoring platform for performance analysis and degradation diagnostics of gas turbine engines was demonstrated [47,53]. These studies underlined the suitability of Gas Path Analysis tools to predict aeroengine performance with a high accuracy.…”

Section: Introductionmentioning

confidence: 99%

Model-Based Dynamic Performance Simulation of a Microturbine Using Flight Test Data

2022

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Microturbines can be used not only in models and education but also to propel UAVs. However, their wider adoption is limited by their relatively low efficiency and durability. Validated simulation models are required to monitor their performance, improve their lifetime, and to design engine control systems. This study aims at developing a numerical model of a micro gas turbine intended for prediction and prognostics of engine performance. To build a reliable zero-dimensional model, the available compressor and turbine maps were scaled to the available test bench data with the least squares method, to meet the performance of the engine achieved during bench and flight tests. A steady-state aeroengine model was implemented in the Gas turbine Simulation Program (GSP) and was compared with experimental operating points. The selected flight data were then used as input for the transient engine model. The exhaust gas temperature (EGT) and fuel flow were chosen as the two key parameters to validate the model, comparing the numerical predicted values with the experimental ones. The observed difference between the model and the flight data was lower than 3% for both EGT and fuel flow.

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Section: Introductionmentioning

confidence: 99%

Model-Based Dynamic Performance Simulation of a Microturbine Using Flight Test Data

2022

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“…In [4] a transient engine model of the Viper 632-43 was created, and different machine learning techniques were used to estimate and predict an engine parameter. Also [5] and [6] describe the proposition and development of an integrated health monitoring platform for performance analysis and degradation diagnostics of gas turbine engines. In [7] a new method for the simulation of gas turbine fuel systems based on an intercomponent volume method has been developed.…”

Section: Introductionmentioning

confidence: 99%

Model-based dynamic performance simulation of a microturbine

Erario

Giorgi

Przysowa

2022

IOP Conf. Ser.: Mater. Sci. Eng.

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Microturbines can be used not only in models and education but also to propel UAVs. However, their wider adoption is limited by their relatively low efficiency and durability. Validated simulation models are required to monitor their performance, improve their lifetime, and design engine control systems. The aim of this study is to develop a numerical model of a micro gas turbine for engine performance predictions and prognostics. To build a reliable zero-dimensional model, the available compressor and turbine maps were scaled to available test bench data with the least-squares method, to meet the performance of the engine achieved during bench and flight tests. A steady-state aeroengine model was then developed and compared with experimental operating points. Selected flight data were then used as input for the transient engine model. The EGT temperature and the fuel flow were chosen as the two key parameters to validate the model, comparing the numerical predicted values with the correspondent experimental ones. The observed difference between the model and flight data was lower than 3% for both EGT and fuel flow.

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“…There are many solutions to this problem for full scale engines [4,5]. An integrated health monitoring platform for performance analysis and degradation diagnostics of gas-turbine engines was demonstrated in our earlier project [6,7]. Ellis et al modeled the deposition of ingested particles on turbine nozzle guide vanes to predict high-pressure turbine degradation using Monte Carlo simulations and a zero-dimensional turbofan model [8].…”

Section: Introductionmentioning

confidence: 99%

Extreme Learning Machine-Based Diagnostics for Component Degradation in a Microturbine

Menga¹,

Mothakani²,

Giorgi³

et al. 2022

Preprint

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Micro turbojets are used for propelling radio-controlled aircraft, aerial targets and personal air vehicles. When compared to full-scale engines, they are characterized by relatively low efficiency and durability. In this context, the degraded performance of gas path components could lead to an unacceptable reduction in the overall engine performance. In this work, a data-driven model based on a conventional Artificial Neural Network (ANN) and an extreme learning machine (ELM) was used for estimating the performance degradation of the micro turbojet. The training datasets containing the performance data of the engine with degraded components were generated using the validated GSP model and the Monte Carlo approach. In particular, compressor and turbine performance degradation were simulated for three different flight regimes. It was confirmed that component degradation has a higher impact in flight than at sea level. Finally, the datasets were used in the training and testing process of the ELM algorithm with four different input vectors. Two vectors had an extensive number of virtual sensors, and the other two were reduced to just fuel flow and Exhaust Gas Temperature. Even with the small number of sensors, the high prediction accuracy of ELM was maintained for takeoff and cruise but was slightly worse for variable flight conditions.

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Development of a real time intelligent health monitoring platform for aero-engine

Cited by 14 publications

References 7 publications

Model-Based Dynamic Performance Simulation of a Microturbine Using Flight Test Data

Model-Based Dynamic Performance Simulation of a Microturbine Using Flight Test Data

Model-based dynamic performance simulation of a microturbine

Extreme Learning Machine-Based Diagnostics for Component Degradation in a Microturbine

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