Vibration Reliability Analysis of Aeroengine Rotor Based on Intelligent Neural Network Modeling Framework

In order to improve the accuracy and calculation efficiency of aeroengine rotor vibration reliability analysis, a time-varying rotor vibration reliability analysis method under the aeroengine operating state is proposed. Aiming at the highly nonlinear and strong coupling of factors affecting the reliability of aeroengine rotor vibration, an intelligent neural network modeling framework (short form-INNMF) is proposed. The proposed method is based on DEA, with QAR information as the analysis data, and four factors including engine working state, fuel/oil working state, aircraft flight state, and external conditions are considered to analyse the rotor vibration reliability. INNMF is based on the artificial neural network (ANN) algorithm through improved particle swarm optimization (PSO) algorithm and Bayesian Regularization (BR) optimization. Through the analysis of the rotor vibration reliability of the B737-800 aircraft during a flight mission from Beijing to Urumqi, the time-varying rotor vibration reliability was obtained, which verified the effectiveness and feasibility of the method. The comparison of INNMF, random forest (RF), and ANN shows that INNMF improves analysis accuracy and calculation efficiency. The proposed method and framework can provide useful references for aeroengine rotor vibration analysis, special treatment, maintenance, and design.

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Time-Varying Matrix Square Roots Solving via Zhang Neural Network and Gradient Neural Network: Modeling, Verification and Comparison

Advances in Neural Networks – ISNN 2009 - Lecture Notes in Computer Science ◽

10.1007/978-3-642-01507-6_2 ◽

2009 ◽

pp. 11-20 ◽

Cited By ~ 1

Author(s):

Yunong Zhang ◽

Yiwen Yang ◽

Ning Tan

Keyword(s):

Neural Network ◽

Network Modeling ◽

Neural Network Modeling ◽

Time Varying ◽

Square Roots ◽

Zhang Neural Network ◽

Gradient Neural Network

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Synthetically Modeling of Thermal Error and Grinding Force Induced Error on a Precision NC Cylindrical Grinding Machine

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.24-25.243 ◽

2007 ◽

Vol 24-25 ◽

pp. 243-248

Author(s):

Hao Wu ◽

Jian Guo Yang ◽

Xiu Shan Wang

Keyword(s):

Neural Network ◽

Bp Neural Network ◽

Network Modeling ◽

Thermal Error ◽

Grinding Force ◽

Modeling Technique ◽

Neural Network Modeling ◽

Thermal Errors ◽

Highly Nonlinear ◽

Grinding Machine

Thermal errors and force-induced errors are two most significant sources of the NC grinding machine inaccuracy. And error compensation technique is an effective way to improve the manufacturing accuracy of the NC machine tools. Effective compensation relies on an accurate error model that can predict the errors exactly during machining. In this paper, a PSO–BP neural network modeling technique has been developed to build the model of the dynamic and highly nonlinear thermal errors and grinding force induced errors. The PSO–BP neural network modeling technique not only enhances the prediction accuracy of the model but also reduces the training time of the neural networks. The radial error of a grinding machine has been reduced from 27 to 8μmafter compensating its thermal error and force-induced error in this paper.

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Artificial neural network modeling of sliding wear

Proceedings of the Institution of Mechanical Engineers Part J Journal of Engineering Tribology ◽

10.1177/1350650120925582 ◽

2020 ◽

pp. 135065012092558

Author(s):

Ivan I Argatov ◽

Young S Chai

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Sliding Wear ◽

Network Modeling ◽

Differential Forms ◽

Neural Network Modeling ◽

Wear Coefficient ◽

Modeling Framework ◽

Artificial Neural Network Modeling ◽

Artificial Neural

A widely used type of artificial neural networks, called multilayer perceptron, is applied for data-driven modeling of the wear coefficient in sliding wear under constant testing conditions. The integral and differential forms of wear equation are utilized for designing an artificial neural network-based model for the wear rate. The developed artificial neural network modeling framework can be utilized in studies of wearing-in period and the so-called true wear coefficient. Examples of the use of the developed approach are given based on the experimental data published recently.

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Modeling of Distillation Tower Temperature Based on D-FNN

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.383-390.1463 ◽

2011 ◽

Vol 383-390 ◽

pp. 1463-1469

Author(s):

Shu Zhi Gao ◽

Jing Yang ◽

Jun Fan

Keyword(s):

Neural Network ◽

Fuzzy Neural Network ◽

Learning Algorithm ◽

Network Modeling ◽

Temperature Control System ◽

Neural Network Modeling ◽

Time Varying ◽

Distillation Process ◽

Dynamic Learning ◽

Fuzzy Neural

Distillation temperature control system is characteristics of nonlinear time-varying and we use dynamic fuzzy neural network to model the temperature of distillation. Firstly, we introduce the structure and algorithm of dynamic fuzzy neural network; Second, after data preprocessing of distillation process, we use dynamic Fuzzy neural network modeling the temperature of distillation. Dynamic fuzzy neural network adopt dynamic learning algorithm, and characteristic of approximation. The simulation results show the effect and accuracy of Dynamic fuzzy neural network model ing method.

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