Nonlinear aeroelastic modeling of aircraft using support vector machine method

Bagherzadeh, Seyed Amin

doi:10.1108/aeat-06-2019-0129

Cited by 6 publications

(3 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the increase of sample size of model processing, machine learning methods are applied to deal with big data by solving and optimizing the weights of shallow networks, so as to obtain a fault diagnosis model with a certain degree of integration and generalization ability (De Giorgi et al , 2019). Typical machine learning methods include support vector machine (SVM), back propagation neural network (BPNN), hidden Markov and radial basis function neural network (Bagherzadeh, 2020; Jiang, 2019; Kouadri et al , 2020; Kim et al , 2019). With the development of artificial intelligence technology, as a branch of machine learning method, deep learning method has been widely applied in speech recognition, image recognition and signal processing (Chen, 2020).…”

Section: Introductionmentioning

confidence: 99%

Semisupervised fault diagnosis of aeroengine based on denoising autoencoder and deep belief network

Wang

Che

2022

AEAT

View full text Add to dashboard Cite

Purpose The purpose of this study is to analyze the intelligent semisupervised fault diagnosis method of aeroengine. Design/methodology/approach A semisupervised fault diagnosis method based on denoising autoencoder (DAE) and deep belief network (DBN) is proposed for aeroengine. Multiple state parameters of aeroengine with long time series are processed to form high-dimensional fault samples and corresponding fault types are taken as sample labels. DAE is applied for unsupervised learning of fault samples, so as to achieve denoised dimension-reduction features. Subsequently, the extracted features and sample labels are put into DBN for supervised learning. Thus, the semisupervised fault diagnosis of aeroengine can be achieved by the combination of unsupervised learning and supervised learning. Findings The JT9D aeroengine data set and simulated aeroengine data set are applied to test the effectiveness of the proposed method. The result shows that the semisupervised fault diagnosis method of aeroengine based on DAE and DBN has great robustness and can maintain high accuracy of fault diagnosis under noise interference. Compared with other traditional models and separate deep learning model, the proposed method also has lower error and higher accuracy of fault diagnosis. Originality/value Multiple state parameters with long time series are processed to form high-dimensional fault samples. As a typical unsupervised learning, DAE is used to denoise the fault samples and extract dimension-reduction features for future deep learning. Based on supervised learning, DBN is applied to process the extracted features and fault diagnosis of aeroengine with multiple state parameters can be achieved through the pretraining and reverse fine-tuning of restricted Boltzmann machines.

show abstract

Section: Introductionmentioning

confidence: 99%

Semisupervised fault diagnosis of aeroengine based on denoising autoencoder and deep belief network

Wang

Che

2022

AEAT

View full text Add to dashboard Cite

show abstract

“…There are many methods that can be used to predict the health states of an aero-engine gas path system, such as the Kalman filter method [5,6], grey theory [7][8][9], neural network method [10,11], support vector machine [12,13], analytic hierarchy process [14,15], hidden Markov model [16,17], and expert knowledge [18][19][20]. A two-way kernel extreme learning machine was proposed to predict the health states of aero-engine gas path system by one parameter [21].…”

Section: Introductionmentioning

confidence: 99%

Health State Prediction of Aero-Engine Gas Path System Considering Multiple Working Conditions Based on Time Domain Analysis and Belief Rule Base

et al. 2021

View full text Add to dashboard Cite

The gas path system is an important part of an aero-engine, whose health states can affect the security of the airplane. During the process of aircraft operation, the gas path system will have different working conditions over time, owing to the change of control parameters. However, the different working conditions which change the symmetry of the system will affect parameters of the health state prediction model for the gas path system. The symmetry of the system will also change. Therefore, it is important to consider the influence of variable working conditions when predicting the health states of gas path system. The accuracy of the health state prediction results of the gas path system will be low if the same evaluation standard is used for different working conditions. In addition, the monitoring data of the gas path system’s health state feature quantity is huge while the fault data which can reflect the health states of the gas path system are poor. Thus, it is difficult to establish a health state prediction model only by using the monitoring data of the gas path system. In order to avoid problems, this paper proposes a health state prediction model considering multiple working conditions based on time domain analysis and a belief rule base. First, working condition is divided by using time domain characteristics. Then, a belief rule base (BRB) theory-based health state prediction model is built, which can fuse expert knowledge and fault monitoring data to improve modeling accuracy. The reference value of the feature is given by the fuzzy C-means algorithm in a model. To decrease the uncertainty of expert knowledge, the covariance matrix adaptive evolution strategy (CMA-ES) is used as the optimization algorithm. Finally, a NASA public dataset without labels is used to verify the proposed health state model. The results show that the proposed health prediction model of a gas path system can accurately realize health state prediction under multiple working conditions.

show abstract

“…UAV system identification is a useful tool for nonlinear aerodynamic modeling. At present, mathematical tools used for aircraft system identification include polynomials [ 12 , 13 ], support vector machines [ 14 , 15 , 16 ], multivariate orthogonal functions [ 17 ], the Extended Kalman Filter method [ 18 , 19 ], and artificial neural networks [ 20 , 21 , 22 , 23 ], and many other mathematical tools are used for aircraft system identification.…”

Section: Introductionmentioning

confidence: 99%

Modeling of a Novel Coaxial Ducted Fan Aerial Robot Combined with Corner Environment by Using Artificial Neural Network

Chen

et al. 2020

Sensors

View full text Add to dashboard Cite

A novel coaxial ducted fan aerial robot with a manipulator is proposed which can achieve some hover operation tasks in a corner environment, such as switching on and off a wall-attached button on the corner. In order to study the aerodynamic interference between the prototype and the environment when the aerial robot is hovering in the corner environment, a method for the comprehensive modeling of the prototype and corner environment based on the artificial neural network is presented. By using the CFD simulation software, the flow field of the prototype at different positions with the corner effect is analyzed. After determining the input, output and structure of the neural network model, the Adam and gradient descent algorithms are selected as the neural network training algorithms, respectively. In addition, to optimize the initial weights and biases of the neural network model, the genetic algorithm is precisely used. The three-dimensional prediction surfaces generated by the three methods of the neural network, kriging surface and the polynomial fitting are compared. The results show that the neural network has high prediction accuracy, and can be applied to the comprehensive modeling of the prototype and the corner environment.

show abstract

Nonlinear aeroelastic modeling of aircraft using support vector machine method

Cited by 6 publications

References 46 publications

Semisupervised fault diagnosis of aeroengine based on denoising autoencoder and deep belief network

Semisupervised fault diagnosis of aeroengine based on denoising autoencoder and deep belief network

Health State Prediction of Aero-Engine Gas Path System Considering Multiple Working Conditions Based on Time Domain Analysis and Belief Rule Base

Modeling of a Novel Coaxial Ducted Fan Aerial Robot Combined with Corner Environment by Using Artificial Neural Network

Contact Info

Product

Resources

About