Engine health monitoring in an aircraft by using Levenberg-Marquardt Feedforward Neural Network and Radial Basis Function Network

Yildirim, M.T.; Bulent, Kurt

doi:10.1109/inista.2016.7571847

Cited by 12 publications

(3 citation statements)

References 3 publications

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“…The difference between the highest EGT temperature reached during the takeoff and the EGT determined by the red reference line is called EGT margin ( Figure 3) [21]. EGT margin is used to obtain information about motor performance.…”

Section: Main Operation Parameters Of the Enginementioning

confidence: 99%

Aircraft Gas Turbine Engine Health Monitoring System by Real Flight Data

Yildirim

Bulent

2018

International Journal of Aerospace Engineering

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Modern condition monitoring-based methods are used to reduce maintenance costs, increase aircraft safety, and reduce fuel consumption. In the literature, parameters such as engine fan speeds, vibration, oil pressure, oil temperature, exhaust gas temperature (EGT), and fuel flow are used to determine performance deterioration in gas turbine engines. In this study, a new model was developed to get information about the gas turbine engine's condition. For this model, multiple regression analysis was carried out to determine the effect of the flight parameters on the EGT parameter and the artificial neural network (ANN) method was used in the identification of EGT parameter. At the end of the study, a network that predicts the EGT parameter with the smallest margin of error has been developed.

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Section: Main Operation Parameters Of the Enginementioning

confidence: 99%

Aircraft Gas Turbine Engine Health Monitoring System by Real Flight Data

Yildirim

Bulent

2018

International Journal of Aerospace Engineering

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“…In the last years, several efforts have been spent to improve the Artificial Intelligence (AI) [1] based tools for monitoring the health status of different aeroengine components, detecting faults and predicting them. The consequence is an increase in flight safety and a decrease in maintenance costs and fuel consumption [2] [3]. Hence the non-condition-based maintenance plans have been replaced (except in some cases [4]) by the more efficient condition-based one known as predictive maintenance [5] [6], which is based on the actual health status of the engine, predictable thanks to adequate sensors installed through the gas-path [7].…”

Section: Introductionmentioning

confidence: 99%

A data-driven approach for health status assessment and remaining useful life prediction of aero-engine

Giorgi

Menga

Mothakani

et al. 2023

J. Phys.: Conf. Ser.

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Artificial Intelligence (AI) algorithms are widely used to improve the health monitoring tools exploited in aeronautics and this result in an increase in reliability and flight safety. Furthermore, better management of maintenance costs is another positive consequence of the application of AI-based health monitoring tools. In this paper, two AI-based health monitoring tools are developed to predict the Remaining Useful Life (RUL) of a fleet of simple turbojet engines VIPER 632 – 43 subject to a compressor degradation process. The AI algorithms used in this work are the Nonlinear Autoregressive Network with Exogenous Inputs (NARX) and the Long Short-Term Memory (LSTM) neural networks, which are two types of Artificial Neural Network (ANN) particularly suitable for time-series forecasting. The data about engine operation in degraded condition necessary to develop the just cited tools are obtained by performing a series of simulations in transient condition in which a degraded state at the compressor is implemented. The Matlab&Simulink software, equipped with the T-MATS Simulink library developed by NASA is used to develop a virtual model of the VIPER 632 – 43 engine and to perform the simulation. Furthermore, an adequate mathematical model is used to simulate the trend of the degradation level during time.

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“…The system predicts some measurable parameters based on flight data and in case of a high difference between measured and predicted values the second part of the tool is activated to detect which component is degraded. In [19] Levenberg-Marquardt Feed-Forward Neural Network (FFNN) and Radial Basis Function Network are exploited for EGT prediction, with better performance obtained by the first cited technique. In this paper, a FFNN is the AI tool used to develop the goal system.…”

Section: Introductionmentioning

confidence: 99%

Feed-Forward Neural Network for health monitoring of a parallel hybrid electric power system

Giorgi

Donateo

Ficarella

et al. 2022

J. Phys.: Conf. Ser.

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Hybrid engines are becoming more and more widespread. Electric energy instead is a valid help to reduce the environmental impact. In hybrid engines, the number of components is higher and this results in a decrease in reliability. With Engine Health Monitoring (EHM) we mean the set of techniques used to monitor the health status of a system based on the values assumed by some related parameters. Artificial Intelligence (AI) methods are widely used nowadays in this discipline. In this paper, an EHM approach was developed to monitor the health status of some components constituting an hybrid turboshaft. The dynamic model of the hybrid electric power system is described in an accompanying paper. Feed-Forward Neural Network (FFNN) is used as AI tool to built the just cited system. The engine modelled with Simulink, was used to perform a series of steady-state simulations implementing a degradation condition in some selected components. The degradation condition was simulated by changing the value of the Performance Parameters (PPs) related to each of the selected components. The results of the simulation were used to obtain a dataset useful to train the FFNN to predict the values of the same PPs in a degraded case.

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Engine health monitoring in an aircraft by using Levenberg-Marquardt Feedforward Neural Network and Radial Basis Function Network

Cited by 12 publications

References 3 publications

Aircraft Gas Turbine Engine Health Monitoring System by Real Flight Data

Aircraft Gas Turbine Engine Health Monitoring System by Real Flight Data

A data-driven approach for health status assessment and remaining useful life prediction of aero-engine

Feed-Forward Neural Network for health monitoring of a parallel hybrid electric power system

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