Using Artificial Neural Networks and Self-Organizing Maps for Detection of Airframe Icing

Johnson, Matthew D.; Rokhsaz, Kamran

doi:10.2514/2.2779

Cited by 19 publications

(4 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some scholars have studied the shape and severity of body ice accumulation using a neural network algorithm [22][23][24]. Dong [25] proposed an ice accumulation fault detection system based on deep learning.…”

Section: Introductionmentioning

confidence: 99%

Study on Icing Environment Judgment Based on Radar Data

et al. 2021

View full text Add to dashboard Cite

As a major threat to aviation flight safety, it is particularly important to make accurate judgments and forecasts of the ice accumulation environment. Radar is widely used in civil aviation and meteorology, and has the advantages of high timeliness and resolution. In this paper, a variety of machine learning methods are used to establish the relationship between radar data and icing index (Ic) to determine the ice accumulation environment. The research shows the following. (1) A linear model was established, based on the scattering rate factor (Zh), radial velocity (v), spectral width (w), velocity standard deviation (σ) detected by 94 GHz millimeter wave radar, and backward attenuation coefficient (β) detected by 905 nm lidar, so linear regression was carried out. After principal component analysis (PCA), the correction determination coefficient of the linear equation was increased from 0.7127 to 0.7240. (2) Ice accumulation was unlikely for samples that were significantly off-center. By clustering the data into three or four categories, the proportion of icing lattice points could be increased from 18.81% to 33.03%. If the clustering number was further increased, the ice accumulation ratio will not be further increased, and the increased classification is reflected in the classification of pairs of noises and the possibility of omission is also increased. (3) Considering the classification and nonlinear factors of ice accumulation risk, the neural network method was used to judge the ice accumulation environment. Two kinds of neural network structures were established for quantitative calculation: Structure 1 first distinguished whether there was ice accumulation, and further calculated the icing index for the points where there was ice accumulation; Structure 2 directly calculated the temperature and relative humidity, and calculated the icing index according to definition. The accuracy of the above two structures could reach nearly 60%, but the quantitative judgment of the ice accumulation index was not ideal. The reasons for this dissatisfaction may be the small number of variables and samples, the interval between time and space, the difference in instrument detection principle, and the representativeness of the ice accumulation index. Further research can be improved from the above four points. This study can provide a theoretical basis for the diagnosis and analysis of the aircraft ice accumulation environment.

show abstract

Section: Introductionmentioning

confidence: 99%

Study on Icing Environment Judgment Based on Radar Data

et al. 2021

View full text Add to dashboard Cite

show abstract

“…In aerospace engineering, NNs can be applied to a large range of complex problems (10) . Neural networks have been used to solve many problems in the aeronautical industry such as the detection and identification of structural damage (11) , the modelling of aerodynamic characteristics from flight data (12,13) , the detection of unanticipated effects such as icing (14)(15)(16) , and autopilot controllers and advanced control laws for applications such as carefree manoeuvring (17,18) , as well as lift and drag aerodynamic coefficients C L and C D prediction (19) .…”

Section: Introductionmentioning

confidence: 99%

New methodology combining neural network and extended great deluge algorithms for the ATR-42 wing aerodynamics analysis

2016

View full text Add to dashboard Cite

The fast determination of aerodynamic parameters such as pressure distributions, lift, drag and moment coefficients from the known airflow conditions (angles of attack, Mach and Reynolds numbers) in real time is still not easily achievable by numerical analysis methods in aerodynamics and aeroelasticity. A flight parameters control system is proposed to solve this problem. This control system is based on new optimisation methodologies using Neural Networks (NNs) and Extended Great Deluge (EGD) algorithms. Validation of these new methodologies is realised by experimental tests using a wing model installed in a wind tunnel and three different transducer systems (a FlowKinetics transducer, an AEROLAB PTA transducer and multitube manometer tubes) to determine the pressure distribution. For lift, drag and moment coefficients, the results of our approach are compared to the XFoil aerodynamics software and the experimental results for different angles of attack and Mach numbers. The main purpose of this new proposed control system is to improve, in this paper, wing aerodynamic performance, and in future to apply it to improve aircraft aerodynamic performance.

show abstract

“…Neural network methods have been used in the aeronautical industry for the following applications: the detection and identification of structural damage [5], fault diagnostics (detection and isolation) leading to compensation of control surface failures [6], the modelling of aerodynamic characteristics from flight data [7,8], generalized reference models for six degrees of freedom motion simulation using global aerodynamic models, including unsteady aerodynamics and dynamic stall [9][10][11], the detection of unanticipated effects such as icing [12], and autopilot controllers and advanced control laws for applications such as carefree manoeuvring [13,14]. Neural network methods have also been used for model identification purposes, based on flight flutter tests.…”

Section: Introductionmentioning

confidence: 99%

Adaptive neuro-fuzzy controllers for an open-loop morphing wing system

Grigorie

Botez

Попов

2009

Proceedings of the Institution of Mechanical Engineers, Part G:

View full text Add to dashboard Cite

A new method for the realization of two neuro-fuzzy controllers for a morphing wing design application is presented here. The controllers' main function is to correlate each set of pressure differences, calculated between the optimized and the reference airfoil, with each of the airfoil deformations produced by the actuators' system. The pressures are calculated at different chord positions and will also be measured during wind tunnel tests. During a first identification phase, the two fuzzy inference systems (FISs) from the controllers' structure are generated for 16 flight conditions characterized by Mach numbers and angles of attack. Next, the FIS are optimized with the Matlab function adaptive neuro-fuzzy inference system (ANFIS) by training over different epochs. Finally, the controllers are validated for the other 33 flight conditions of the open-loop morphing wing system. This is the first time that such a method of relating the pressure differences to airfoil displacements has been conceived and used in an open-loop morphing wing controller system.

show abstract

Using Artificial Neural Networks and Self-Organizing Maps for Detection of Airframe Icing

Cited by 19 publications

References 15 publications

Study on Icing Environment Judgment Based on Radar Data

Study on Icing Environment Judgment Based on Radar Data

New methodology combining neural network and extended great deluge algorithms for the ATR-42 wing aerodynamics analysis

Adaptive neuro-fuzzy controllers for an open-loop morphing wing system

Contact Info

Product

Resources

About