Challenges, Issues, and Lessons Learned Implementing Prognostics for Propulsion Systems

Hess, Andrew; Frith, Peter; Suarez, E.

doi:10.1115/gt2006-91279

Cited by 8 publications

(6 citation statements)

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“…Finally, the resulting flow field of a jet in crossflow is similar to that of V/STOL aircrafts. Hobson et al [26] investigated the performance of a crossfiow fan propulsion system for V/STOL aircrafts while Hess et al [27] discussed important issues and challenges for such propulsion systems. This investigation examines experimentally the effect of blowing ratio of fans in crossfiow.…”

Section: Effects Of Swirl Velocities From Fan Assemblies Mounted On Lmentioning

confidence: 99%

Effects of Swirl Velocities From Fan Assemblies Mounted on Lifting Surfaces

Terzis

Kazakos

Papadopoulos

et al. 2010

Journal of Engineering for Gas Turbines and Power

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The penetration of a jet of fluid into a traversal moving stream is a basic configuration of a wide range of engineering applications, such as film cooling and V/STOL aircrafts. This investigation examines experimentally the effect of blowing ratio of fans in crossflow, and numerically, the effect of the swirl velocity of jets in crossflow, downstream of the injection hole. The experimental results indicated an agreement with typically straight jets in crossflow (no vorticity), illustrating that the trace of the jet, remains close to the wall and subsequently enhance cooling at low blowing ratios in the case of turbine blade applications. However, the rotation of the jet results in an imparity between the two parts of the counter rotating vortex pair and as a consequence, the injected fluid not only bends in the direction of the main stream but also diverts in the direction of the rotation in order to conserve its angular momentum. The induction of the swirl velocity on the injected jet destructs one of the two parts of the kidney vortex, which entrains fluid from the crossflow to the jet promoting the mixing between the two fluids while the trace of a swirled jet remains closer to the wall downstream of the injection hole. Finally, the use of contrarotating jet or fan configurations reduces the wall shear stress in a very great extent, leading to better thermal protection of turbine blades, as well as cancels out the yaw torques of each fan separately, resulting in better flight control of typical lift surface.

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Section: Effects Of Swirl Velocities From Fan Assemblies Mounted On Lmentioning

confidence: 99%

Effects of Swirl Velocities From Fan Assemblies Mounted on Lifting Surfaces

Terzis

Kazakos

Papadopoulos

et al. 2010

Journal of Engineering for Gas Turbines and Power

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“…Unlike Bangert (2020); Li and Nilkitsaranont (2009), our approach relies on a more powerful model such as DTs to forecast GTs' future conditions. Various other GT prognostic techniques have been investigated, (DePold and Gass, 1999;Brotherton et al, 2000;Roemer and Kacprzynski, 2000;Byington et al, 2002;Brotherton et al, 2002;Roemer et al, 2006;Hess et al, 2006). Most of these techniques are experience-based prognostics, model-based prognostics, evolutionary prognostics, neural networks, state estimator prognostics, rule-based expert systems, and fuzzy logic-based methods.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approaches for the Prediction of Gas Turbine Transients

Fadja,

Cota,

Bertasi

et al. 2024

Journal of Computer Science

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Gas Turbine (GT) emergency shutdowns can lead to energy production interruption and may also reduce the lifespan of a turbine. In order to remain competitive in the market, it is necessary to improve the reliability and availability of GTs by developing predictive maintenance systems that are able to predict future conditions of GTs within a certain time. Predicting such situations not only helps to take corrective measures to avoid service unavailability but also eases the process of maintenance and considerably reduces maintenance costs. Huge amounts of sensor data are collected from (GTs) making monitoring impossible for human operators even with the help of computers. Machine learning techniques could provide support for handling large amounts of sensor data and building decision models for predicting GT future conditions. The paper presents an application of machine learning based on decision trees and k-nearest neighbors for predicting the rotational speed of gas turbines. The aim is to distinguish steady states (e.g., GT operation at normal conditions) from transients (e.g., GT trip or shutdown). The different steps of a machine learning pipeline, starting from data extraction to model testing are implemented and analyzed. Experiments are performed by applying decision trees, extremely randomized trees, and k-nearest neighbors to sensor data collected from GTs located in different countries. The trained models were able to predict steady state and transient with more than 93% accuracy. This research advances predictive maintenance methods and suggests exploring advanced machine learning algorithms, real-time data integration, and explainable AI techniques to enhance gas turbine behavior understanding and develop more adaptable maintenance systems for industrial applications.

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“…Furthermore, there are various other works exploring this subject, such as [14][15][16]. Those authors expose investigation of techniques that aim to reduce machinery periodic inspections, which collaborates with the desire to maximize aircraft availability [17]. Although many aircraft turbine fault diagnostic methods have been developed [18], the fuzzy logic system (FLS) holds unexplored potential.…”

Section: Introductionmentioning

confidence: 99%

An enhanced aircraft engine gas path diagnostic method based on upper and lower singleton type-2 fuzzy logic system

Calderano

Ribeiro

Amaral

et al. 2019

J Braz. Soc. Mech. Sci. Eng.

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The gas turbine is the most common engine used in the majority of commercial aircraft. Regarding the economic and social importance of aviation, methods that can identify faults in gas turbines with precision are relevant. Aiming to detect and classify the gas turbine faults, this work uses the upper and lower singleton type-2 fuzzy logic system trained by steepest descent method. Succeeding the model presentation, comparisons are performed with other models proposed in the literature to diagnose gas turbine faults. The investigated data set was obtained from the software Propulsion Diagnostic Method Evaluation Strategy, which was developed by the National Aeronautics and Space Administration for benchmarking purposes in aviation gas turbines. The experimental results show that the upper and lower singleton type-2 fuzzy logic system model had a greater detection and classification performance than the models reported in the literature.

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Challenges, Issues, and Lessons Learned Implementing Prognostics for Propulsion Systems

Cited by 8 publications

References 0 publications

Effects of Swirl Velocities From Fan Assemblies Mounted on Lifting Surfaces

Effects of Swirl Velocities From Fan Assemblies Mounted on Lifting Surfaces

Machine Learning Approaches for the Prediction of Gas Turbine Transients

An enhanced aircraft engine gas path diagnostic method based on upper and lower singleton type-2 fuzzy logic system

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