A diagnostics tool for aero-engines health monitoring using machine learning technique

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

doi:10.1016/j.egypro.2018.08.109

Cited by 43 publications

(22 citation statements)

References 18 publications

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“…In this work to test the effectiveness of the developed tool, a representative model of a turbojet is chosen, with a static thrust of 17800 N at sea level; all the other details are reported in [11,12]. The present study was carried out based on 3 samples flights denoted by Flight #1, Flight #2 and Flight #3 [6].…”

Section: Aero-engine Model and Data Generation Of The Test Casesmentioning

confidence: 99%

“…It has been built a database of engine performance data coming from an healthy engine model. The synthetic data are generated with the combination of two software, ONX and AEDSYS [13]; the first one is used to model engine characteristics in design condition and this model has been validated on experimental data [11,12], the second one is able to test the modelled engine in different mission conditions, varying altitude, Mach and thrust request.…”

Section: Aero-engine Model and Data Generation Of The Test Casesmentioning

confidence: 99%

“…The SET #A1 involves only flight condition, namely Mach number and altitude, in the input layer of the network [11,12]. This dataset is not dependent from engine status but there is an evident lack about the user power demand.…”

Section: Simulation Of Engine Performancementioning

confidence: 99%

“…We trained the neural network to identify the synthetic engine health status; in a first attempt (SET #B1) fuel mass flow rate and exhaust gas temperature were used to identify the engine health index [11,12] then in the SET #B2 we added the Mach number to test the utility of flight condition. In the last configuration in the SET #B3 the burner exit temperature Tt4, suggestive of the user request, has been introduced.…”

Section: Engine Component Health Status Diagnosticsmentioning

confidence: 99%

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

2018

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In this paper an integrated heath monitoring platform is proposed and developed for performance analysis and degradation diagnostics of gas turbine engines. In a first approach the numerical tool is able to predict engine measurable data from flight data, in order to create a dataset of expected values. Then, in the case of a mismatch between expected values and measured data coming from a real engine, a second part of the tool can be activated to detect the component under degradation. In order to evaluate the performance prediction artificial neural networks (ANN) have been implemented. The tool is able to recognize the degradation due to compressor fouling and turbine erosion. Synthetic data generation has been carried out to show how the degradation effects can affect the engine performance. The used data have been generated with a model based on gas path analysis. The training of the model is focused on components deterioration due to a combination of fouling and erosion. Different scenarios have been compared in order to carry out a sensitivity analysis and to choose the best parameters for the network input and output. Obviously the knowledge of the real engine health status can be crucial for maintenance and fleet management decisions.

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Section: Aero-engine Model and Data Generation Of The Test Casesmentioning

confidence: 99%

Section: Aero-engine Model and Data Generation Of The Test Casesmentioning

confidence: 99%

Section: Simulation Of Engine Performancementioning

confidence: 99%

Section: Engine Component Health Status Diagnosticsmentioning

confidence: 99%

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

2018

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“…Another application of SVM for the early failure prediction in the oil and gas industry is shown in [13]. In [14], a monitoring platform using Artificial Neural Network and the Support Vector Machine is proposed and applied to the prediction of the performance of aeronautical engines and health diagnosis. Nevertheless, conventional algorithms are also created to perform two-class or multi-class classification tasks.…”

Section: Introductionmentioning

confidence: 99%

Metaheuristics and Support Vector Data Description for Fault Detection in Industrial Processes

et al. 2020

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In this study, a system for faults detection using a combination of Support Vector Data Description (SVDD) with metaheuristic algorithms is presented. The presented approach is applied to a real industrial process where the set of measured faults is scarce. The original contribution in this work is the industrial context of application and the comparison of swarm intelligence algorithms to optimize the SVDD hyper-parameters. Four recent metaheuristics are compared hereby to solve the corresponding optimization problem in an efficient manner. These optimization techniques are then implemented for fault detection in a multivariate industrial process with non-balanced data. The obtained numerical results seem to be promising when the considered optimization techniques are combined with SVDD. In particular, the Spotted Hyena algorithm outperforms other metaheuristics reaching values of F1 score near 100% in fault detection.

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Ensemble of hybrid neural networks to compensate for epistemic uncertainties: a case study in system prognosis

Dourado

Viana

2022

Soft Comput

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A diagnostics tool for aero-engines health monitoring using machine learning technique

Cited by 43 publications

References 18 publications

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

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

Metaheuristics and Support Vector Data Description for Fault Detection in Industrial Processes

Ensemble of hybrid neural networks to compensate for epistemic uncertainties: a case study in system prognosis

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