The Identification of the Natural Frequency of Rolling Bearing Rotor System Based on Combined Genetic Neural Network

During identifying the natural frequency of the rolling bearing rotor system, due to the complex non-linear relationship between the factors which influence the natural frequency, it is hard to establish a complete and accurate theoretical model. Based on the self-learning ability and approximation of non-linear mapping capability of the artificial neural network (ANN) and the powerful ability of global optimization of the genetic algorithm (GA), the paper establishes combined genetic neural network (GA–ANN) through optimizing the ANN by GA. This method establishes the mapping between a rolling bearing rotor system natural frequency and the various parameters, which reduces the calculation of the workload greatly for the study of the similar rotor structure’s natural frequency. Through using the network model to predict the natural frequency of rolling bearing rotor system under different parameters, we finally find that the predicted values are in good agreement with the experimental data, which indicates that the method is powerful in identification.

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Forecasting of the Fatigue Life of Metal Weld Joints Based on Combined Genetic Neural Network

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.439-440.195 ◽

2010 ◽

Vol 439-440 ◽

pp. 195-201 ◽

Cited By ~ 3

Author(s):

Dan Ma ◽

Zhan Qing Chen ◽

Xian Lei Shan

Keyword(s):

Neural Network ◽

Fatigue Life ◽

Forecast Accuracy ◽

Linear Mapping ◽

Learning Ability ◽

Weld Joints ◽

Non Linear ◽

Genetic Neural Network ◽

Artificial Neural Network Ann ◽

Metal Weld

For metal weld joints, due to the complex non-linear relationship among the factors which influence the fatigue performance, so it is hard to establish an accurate theoretical model to forecast its fatigue life. Based on the self-learning ability and approximation of non-linear mapping capability of the artificial neural network (ANN) and the powerful ability of global optimization of the genetic algorithm (GA), the paper through optimizing the ANN by GA, establishes combined genetic neural network (GA–ANN). The method establishes the mapping relationship between the fatigue life of metal weld joints and a variety of influencing factors, having greatly increased the computational efficiency for the fatigue life of metal weld joints, also had a higher forecast accuracy. The superiority of this method had been tested by the forecast of the fatigue life of weld joints in different process parameters, the new method to forecast the fatigue life of metal weld joints is proposed.

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Identification of Natural Frequency of Bearing Rotor Based on GA-SVM

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.443-444.27 ◽

2012 ◽

Vol 443-444 ◽

pp. 27-33

Author(s):

Tian Ran Ma ◽

Fei Hu Qin ◽

Rui Xue Liu ◽

Feng Jie Zhang

Keyword(s):

Experimental Data ◽

Support Vector Machine ◽

Natural Frequency ◽

Rolling Bearing ◽

Linear Mapping ◽

Support Vector ◽

Non Linear ◽

Experimental Values ◽

Rotor Structure ◽

Good Agreement

During identify natural frequency of bearing rotor, due to the complex non-linear relationship among the factors which influence natural frequency, so it is hard to establish a complete and accurate theoretical model. Based on the generalization and approximation of non-linear mapping capability of support vector machine (SVM) and the powerful ability of global optimization of the genetic algorithm (GA), the paper through optimizing the SVM by GA, establishes combined Genetic Support Vector Machine (GA-SVM). The method establishes the mapping between the natural frequency of a rolling bearing rotor and the various parameters, which reduces the rotor structure for the study similar to the natural frequency of the calculation of the workload greatly. Using the model to indentify the natural frequency of bearing rotor under different parameters, then compare identification value with experimental values shows that projections in good agreement with the experimental data.

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Application of Artificial Neural Network in Distillation System: A Critical Review of Recent Progress

Asian Journal of Research in Computer Science ◽

10.9734/ajrcos/2021/v11i130252 ◽

2021 ◽

pp. 8-16

Author(s):

Chunli Li ◽

Chunyu Wang

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Dynamic Performance ◽

Linear Mapping ◽

Research Progress ◽

Non Linear ◽

Input Parameters ◽

Multiple Variables ◽

Artificial Neural ◽

Artificial Neural Network Ann

Distillation is a unit operation with multiple input parameters and multiple output parameters. It is characterized by multiple variables, coupling between input parameters, and non-linear relationship with output parameters. Therefore, it is very difficult to use traditional methods to control and optimize the distillation column. Artificial Neural Network (ANN) uses the interconnection between a large number of neurons to establish the functional relationship between input and output, thereby achieving the approximation of any non-linear mapping. ANN is used for the control and optimization of distillation tower, with short response time, good dynamic performance, strong robustness, and strong ability to adapt to changes in the control environment. This article will mainly introduce the research progress of ANN and its application in the modeling, control and optimization of distillation towers.

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A Prediction Model of Deflection of Fixing Thin Parts

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.217-219.1526 ◽

2012 ◽

Vol 217-219 ◽

pp. 1526-1529

Author(s):

Yu Mei Liu ◽

Wen Ping Liu ◽

Zhao Liang Jiang ◽

Zhi Li

Keyword(s):

Neural Network ◽

Experimental Data ◽

Artificial Neural Network ◽

Prediction Model ◽

Simulated Data ◽

Artificial Neural ◽

Predicted Values ◽

Artificial Neural Network Ann ◽

Abaqus Software

A prediction model of deflection is presented. The Artificial Neural Network (ANN) is adopted, and ANN establishes the mapping relation between the clamping forces and the position of fixing and the value of deflection. The results of simulation of Abaqus software is used for Training and querying an ANN. The predicted values are in agreement with simulated data and experimental data.

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Comparing the Prediction Capabilities of Artificial Neural Network (ANN) and Nonlinear Regression Models in Pet-Poy Yarn Characteristics and Optimization of Yarn Production Conditions

Journal of Engineered Fibers and Fabrics ◽

10.1177/155892501701200302 ◽

2017 ◽

Vol 12 (3) ◽

pp. 155892501701200 ◽

Cited By ~ 2

Author(s):

Kenan Yıldirimm ◽

Hamdi Ogut ◽

Yusuf Ulcay

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Linear Regression ◽

Nonlinear Regression ◽

Fine Tuning ◽

Draw Force ◽

Non Linear ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Production Conditions

In the manufacture of yarn, predicting the effect of changing production conditions is vital to reducing defects in the end product. This study compares, for the first time, non-linear regression and artificial neural network (ANN) models in predicting 10 yarn properties shaped by the influence of winding speed, quenching air temperature and/or quenching air speed during production. A multilayer perceptron ANN model was created by training 81 patterns using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. The hyperbolic tangent, or TanH, activation function and logistic activation functions were used for the hidden and output layers respectively. Results showed that the ANN approach exhibited a greater prediction capability over the nonlinear regression method. ANN simultaneously predicted all of the 10 final properties of a yarn; tensile strength, tensile strain, draw force, crystallinity ratio, dye uptake based on the colour strengths (K/S), brightness, boiling shrinkage and yarn evenness, more accurately than the non-linear regression model (R2=0.97 vs. R2=0.92). These results lend support to the idea that the ANN analysis combined with optimization can be used successfully to prevent production defects by fine tuning the production environment.

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An Improved Artificial Neural Network for Recognition of Beam Pump Dynamometer Card

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.524-527.1331 ◽

2012 ◽

Vol 524-527 ◽

pp. 1331-1334

Author(s):

Jun Ni ◽

Zhan Li Ren ◽

Guo Qing Han

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Back Propagation ◽

Linear Mapping ◽

Back Propagation Algorithm ◽

Propagation Algorithm ◽

Beam Pump ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Dynamometer Card

Beam pump dynamometer card plays an important role in identifying the production state of oil wells. With an ability to reflect any non-linear mapping relationship, the artificial neural network (ANN) can be used in pattern recognition. This paper illuminates ANN realization in identifying fault kinds of dynamometer cards, including a back-propagation algorithm, characteristics of the Dynamometer card and some examples. It is concluded that the buildup of a neural network and the abstract of dynamometer cards are important to successful application.

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Prediction the Vapor-Liquid Equilibria of CO2-Containing Binary Refrigerant Mixtures Using Artificial Neural Networks

ISRN Chemical Engineering ◽

10.1155/2013/930484 ◽

2013 ◽

Vol 2013 ◽

pp. 1-11 ◽

Cited By ~ 3

Author(s):

Ahmad Azari ◽

Saeid Atashrouz ◽

Hamed Mirshekar

Keyword(s):

Neural Network ◽

Experimental Data ◽

Ann Model ◽

Artificial Neural ◽

Predicted Values ◽

Artificial Neural Network Ann ◽

Vapor Liquid Equilibria ◽

Binary Refrigerant ◽

Binary Refrigerant Mixtures ◽

Vapor Liquid

Artificial neural network (ANN) technique has been applied for estimation of vapor-liquid equilibria (VLE) for eight binary refrigerant systems. The refrigerants include difluoromethane (R32), propane (R290), 1,1-difluoroethane (R152a), hexafluoroethane (R116), decafluorobutane (R610), 2,2-dichloro-1,1,1-trifluoroethane (R123), 1-chloro-1,2,2,2-tetrafluoroethane (R124), and 1,1,1,2-tetrafluoroethane (R134a). The related experimental data of open literature have been used to construct the model. Furthermore, some new experimental data (not applied in ANN training) have been used to examine the reliability of the model. The results confirm that there is a reasonable conformity between the predicted values and the experimental data. Additionally, the ability of the ANN model is examined by comparison with the conventional thermodynamic models. Moreover, the presented model is capable of predicting the azeotropic condition.

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A Non-Linear Auto-Regressive With Exogenous Inputs (NARX) Artificial Neural Network (ANN) Model for Building Thermal Load Prediction

ASME 2019 13th International Conference on Energy Sustainability ◽

10.1115/es2019-3923 ◽

2019 ◽

Author(s):

Byeongho Yu ◽

Dongsu Kim ◽

Heejin Cho ◽

Pedro Mago

Keyword(s):

Neural Network ◽

Machine Learning ◽

Artificial Neural Network ◽

Nonlinear System ◽

Prediction Model ◽

Thermal Load ◽

Ann Model ◽

Load Prediction ◽

Non Linear ◽

Artificial Neural Network Ann

Abstract Thermal load prediction is a key part of energy system management and control in buildings, and its accuracy plays a critical role to improve and maintain building energy performance and efficiency. To address this issue, various types of prediction model have been considered and studied, such as physics-based, statistical, and machine learning models. Physical models can be accurate but require extended lead time for model development. Statistical models are relatively simple to develop and require less computation time than other models, but they may not provide accurate results for complex energy systems with an intricate nonlinear dynamic behavior. This study proposes an Artificial Neural Network (ANN) model, one of the prevalent machine learning methods to predict building thermal load, combining with the concept of Non-linear Auto-Regression with Exogenous inputs (NARX). NARX-ANN prediction model is distinguished from typical ANN models due to the fact that the NARX concept can address nonlinear system behaviors effectively based on recurrent architectures and time indexing features. To examine the suitability and validity of NARX-ANN model for building thermal load prediction, a case study is carried out using field data of an academic campus building at Mississippi State University. Results show that the proposed NARX-ANN model can provide an accurate prediction performance and effectively address nonlinear system behaviors in the prediction.

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