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Crack Fault Diagnosis and Location Method for a Dual-Disk Hollow Shaft Rotor System Based on the radial basis function network and Pattern Recognition Neural network

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Chinese Journal of Mechanical Engineering 2023年第2期36卷 180-197页

作者： Yuhong Jin Lei Hou Zhenyong Lu Yushu Chen School of Astronautics Harbin Institute of TechnologyHarbin 150001China Institute of Dynamics and Control Science Shandong Normal UniversityJi’nan 250014China

The crack fault is one of the most common faults in the rotor system,and researchers have paid close attention to its fault ***,most studies focus on discussing the dynamic response characteristics caused by the crack rather than estimating the crack depth and position based on the obtained vibration *** this paper,a novel crack fault diagnosis and location method for a dual-disk hollow shaft rotor system based on the radial basis function(RBF)network and Pattern recognition neural network(PRNN)is ***,a rotor system model with a breathing crack suitable for a short-thick hollow shaft rotor is established based on the finite element method,where the crack's periodic opening and closing pattern and different degrees of crack depth are ***,the dynamic response is obtained by the harmonic balance *** adjusting the crack parameters,the dynamic characteristics related to the crack depth and position are analyzed through the amplitude-frequency responses and waterfall *** analysis results show that the first critical speed,first subcritical speed,first critical speed amplitude,and super-harmonic resonance peak at the first subcritical speed can be utilized for the crack fault *** on this,the RBF network and PRNN are adopted to determine the depth and approximate location of the crack respectively by taking the above dynamic characteristics as *** results show that the proposed method has high fault diagnosis *** research proposes a crack detection method adequate for the hollow shaft rotor system,where the crack depth and position are both unknown.

关键词： Hollow shaft rotor Breathing crack radial basis function network Pattern recognition neural network Machine learning

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Diffuseness effect and radial basis function network for optimizing α decay calculations

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Chinese Physics C 2021年第2期45卷 459-466页

作者： Na-Na Ma Xiao-Jun Bao Hong-Fei Zhang School of Nuclear Science and Technology Lanzhou UniversityLanzhou 730000China Department of Physics Collaborative Innovation Center for Quantum Effectsand Key Laboratory of Low Dimensional Quantum Structures and Quantum Control of Ministry of EducationHunan Normal UniversityChangsha 410081China

A radial basis function network(RBFN)approach is adopted for the first time to optimize the calculation of$\alpha$decay half-life in the generalized liquid drop model(GLDM),while concurrently incorporating the surface diffuseness *** calculations presented herein agree closely with the experimental half-lives for 68 superheavy nuclei(SHN),achieving a remarkable reduction of 40%in the root-mean-square(rms)deviations of ***,using the RBFN method,the half-lives for four SHN isotopes,252-288Rf,272-310Fl,286-316119,and 292-318120,are predicted using the improved GLDM with the diffuseness correction and the decay energies from WS4 and FRDM as ***,we conclude that the diffuseness effect should be embodied in the proximity ***,increased application of neural network methods in nuclear reaction studies is encouraged.

关键词： αdecay radial basis function network the surface diffuseness effect

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Application of the optimal Latin hypercube design and radial basis function network to collaborative optimization

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Journal of Marine Science and Application 2007年第3期6卷 24-32页

作者： ZHAO Min CUI Wei-cheng State Key Laboratory of Ocean Engineering Shanghai Jiao Tong University Shanghai 200030 China China Ship Scientific Research Center P.O.Box 116 Wuxi 214082 China

Improving the efficiency of ship optimization is crucial for modem ship design. Compared with traditional methods, multidisciplinary design optimization （MDO） is a more promising approach. For this reason, Collaborative Optimization （CO） is discussed and analyzed in this paper. As one of the most frequently applied MDO methods, CO promotes autonomy of disciplines while providing a coordinating mechanism guaranteeing progress toward an optimum and maintaining interdisciplinary compatibility. However, there are some difficulties in applying the conventional CO method, such as difficulties in choosing an initial point and tremendous computational requirements. For the purpose of overcoming these problems, optimal Latin hypercube design and radial basis function network were applied to CO. Optimal Latin hypercube design is a modified Latin Hypercube design. radial basis function network approximates the optimization model, and is updated during the optimization process to improve accuracy. It is shown by examples that the computing efficiency and robustness of this CO method are higher than with the conventional CO method.

关键词： multidisciplinary design optimization (MDO) collaborative optimization (CO) optimal Latin hypercube design radial basis function network approximation

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An Intelligent PID Controller Based on Variable Structure radial basis function network

An Intelligent PID Controller Based on Variable Structure Ra...

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2009中国控制与决策会议

作者： Jianchuan Yin,Jiangqiang Hu and Renxiang Bu College of Navigation,Dalian Maritime University,Dalian 116026,China

＜正＞A sequential learning algorithm for constructing variable structure radial basis function（RBF） network is *** algorithm learns samples sequentially,with both network structure and connecting parameters are adjusted *** resulting RBF network is used for system identification in PID control *** RBF-network-based PID controller simplifies modeling procedure by learning input-output samples while keep the advantages of traditional PID controller *** results of ship course control simulation demonstrate the applicability and effectiveness of the intelligent PID control strategy.

关键词： radial basis function network variable structure sequential learning PTD control

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radial basis function networks Applied in Bacterial Classification Based on MALDI-TOF-MS

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Chemical Research in Chinese Universities 2002年第4期18卷 453-457页

作者： HARRINGTON Peter de B. VOORHEES Kent J. REES Jon Ohio University Center for Intelligent Chemical Instrumentation Department of Chemistry and Biochemistry Ohio UniversityDepartment of Chemistry and Geochemistry Colorado School of MinesDepartment of Chemistry and Geochemistry Colorado School of Mines Athens Ohio 45701-2979 USA Golden CO 80401 USAGolden CO 80401 USA

The radial basis function networks were applied to bacterial classification based on the matrix-assisted laser desorption/ionization time-of-flight mass spectrometric (MALDI-TOF-MS) data. The classification of bacteria cultured at different time was discussed and the effect of the network parameters on the classification was investigated. The cross-validation method was used to test the trained networks. The correctness of the classification of different bacteria investigated changes in a wide range from 61.5% to 92.8%. Owing to the complexity of biological effects in bacterial growth, the more rigid control of bacterial culture conditions seems to be a critical factor for improving the rate of correctness for bacterial classification.

关键词： radial basis function network Matrix-assisted laser desorption/ionization Time-of-flight mass spectrometry Bacterium Classification

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Dynamical pattern recognition for univariate time series and its application to an axial compressor

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Control Theory and Technology 2024年第1期22卷 39-55页

作者： Jingtao Hu Weiming Wu Zejian Zhu Cong Wang Center for Intelligent Medical Engineering School of Control Science and TechnologyShandong UniversityJinan250061ShandongChina School of Automation Science and Engineering South China University of TechnologyGuangzhou510641GuangdongChina

In this paper, a learning and recognition approach is proposed for univariate time series composed of output measurements of general nonlinear dynamical systems. Firstly, a class of dynamical systems in the canonical form is derived to describe the univariate time series by introducing coordinate transformation. An observer-based deterministic learning technique is then adopted to achieve dynamical modeling of the associated transformed systems of the training univariate time series, and the modeling results in the form of radial basis function network (RBFN) models are stored in a pattern library. Subsequently, multiple observer-based dynamical estimators containing the RBFN models in the pattern library are constructed for a test univariate time series, and a recognition decision scheme is proposed by the derived recognition indicator. On this basis, more concise recognition conditions are provided, which is beneficial for verifying the recognition results. Finally, simulation studies on the Rossler system and aero-engine stall warning verify the effectiveness of the proposed approach.

关键词： Dynamical pattern recognition Deterministic learning Stall warning radial basis function network Sampled-data observer

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A Bio-Inspired Global Finite Time Tracking Control of Four-Rotor Test Bench System

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Computers, Materials & Continua 2018年第12期57卷 365-388页

作者： Rooh ul Amin Irum Inayat Li Aijun Shahaboddin Shamshirband Timon Rabczuk School of Automation Northwestern Polytechnical UniversityXi’an710072China Department of Computer Science National University of Computer and Emerging SciencesIslamabad44000Pakistan Department for Management of Science and Technology Development Ton Duc Thang UniversityHo Chi Minh CityVietnam Faculty of Information Technology Ton Duc Thang UniversityHo Chi Minh CityVietnam Institute of Structural Mechanics Bauhaus University Weimar99423WeimarGermany

A bio-inspired global finite time control using global fast-terminal sliding mode controller and radial basis function network is presented in this article,to address the attitude tracking control problem of the three degree-of-freedom four-rotor hover *** proposed controller provides convergence of system states in a predetermined finite time and estimates the unmodeled dynamics of the four-rotor *** model of the four-rotor system is derived with Newton’s force *** unknown dynamics of four-rotor systems are estimated using radial basis *** bio-inspired global fast terminal sliding mode controller is proposed to provide chattering free finite time error convergence and to provide optimal tracking of the attitude angles while being subjected to unknown *** global stability proof of the designed controller is provided on the basis of Lyapunov stability *** proposed controller is validated by(i)conducting an experiment through implementing it on the laboratory-based hover system,and(ii)through *** of the proposed control scheme is also compared with classical and intelligent *** performance comparison exhibits that the designed controller has quick transient response and improved chattering free steady state *** proposed bioinspired global fast terminal sliding mode controller offers improved estimation and better tracking performance than the traditional *** addition,the proposed controller is computationally cost effective and can be implanted on multirotor unmanned air vehicles with limited computational processing capabilities.

关键词： Bio-inspired global fast terminal sliding mode controller attitude tracking radial basis function network four-rotor hover system

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A fault diagnosis method of reciprocating compressor based on sensitive feature evaluation and artificial neural network

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High Technology Letters 2015年第4期21卷 422-428页

作者：兴成宏 Xu Fengtian Yao Ziyun Li Haifeng Zhang Jinjie Diagnosis and Self-Recovery Engineering Research Center Beijing University of Chemical Technology PetroChina Beijing Gas Pipeline Co. Ltd. PetroChina Fushun Petrochemical Company Detergent Chemical Plant

A method combining information entropy and radial basis function network is proposed for fault automatic diagnosis of reciprocating *** at the current situation that the accuracy rate of reciprocating compressor fault diagnosis which depends on manual work in engineering is very low,we apply information entropy evaluation to select the sensitive features and make clear the corresponding relationship of characteristic parameters and *** method could reduce the feature ***,a complete fault diagnosis architecture has been built combining with radial basis function network which has the fast and efficient *** to the test results using experimental and engineering data,it is observed that the proposed fault diagnosis method improves the accuracy of fault automatic diagnosis effectively and it could improve the practicability of the monitoring system.

关键词： information entropy radial basis function network fault automatic diagnosis re-ciprocating compressor sensitive feature

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An ECT System Based on Improved RBF network and Adaptive Wavelet Image Enhancement for Solid/Gas Two-phase Flow

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Chinese Journal of Chemical Engineering 2012年第2期20卷 359-367页

作者：陈夏胡红利张娟周屈兰 State Key Laboratory of Electrical Insulation and Power Equipment Xi'an Jiaotong University

Electrical capacitance tomography(ECT) is a non-invasive imaging technique that aims at visualizing the cross-sectional permittivity distribution and phase distribution of solid/gas two-phase flow based on the measured *** solve the nonlinear and ill-posed inverse problem:image reconstruction of ECT system,this paper proposed a new image reconstruction method based on improved radial basis function(RBF) neural network combined with adaptive wavelet image ***,an improved RBF network was applied to establish the mapping model between the reconstruction image pixels and the capacitance values ***,for better image quality,adaptive wavelet image enhancement technique was emphatically analyzed and studied,which belongs to a space-frequency analysis method and is suitable for image *** multi-level wavelet decomposition,edge points of the image produced from RBF network can be determined based on the neighborhood property of each sub-band;noise distribution in the space-frequency domain can be estimated based on statistical characteristics;after that a self-adaptive edge enhancement gain can be ***,the image is reconstructed with adjusting wavelet *** this paper,a 12-electrode ECT system and a pneumatic conveying platform were built up to verify this image reconstruction *** results demonstrated that adaptive wavelet image enhancement technique effectively implemented edge detection and image enhancement,and the improved RBF network and adaptive wavelet image enhancement hybrid algorithm greatly improved the quality of reconstructed image of solid/gas two-phase flow [pulverized coal(PC)/air].

关键词： electrical capacitance tomography.image reconstruction radial basis function network wavelet imageenhance ment

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An optimized grey wolf optimizer based on a mutation operator and eliminating-reconstructing mechanism and its apphcation

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Frontiers of Information Technology & Electronic Engineering 2017年第11期18卷 1705-1719页

作者： Xiao-qing ZHANG Zheng-feng MING School of Mechano-Electronic Engineering Xidian University Xi'an 710071 China School of Physics and Electronic Engineering Xianyang Normal University Xianyang 712000 China

Due to its simplicity and ease of use, the standard grey wolf optimizer （GWO） is attracting much attention. However, due to its imperfect search structure and possible risk of being trapped in local optima, its application has been limited. To perfect the performance of the algorithm, an optimized GWO is proposed based on a mutation operator and eliminating-reconstructing mechanism （MR-GWO）. By analyzing GWO, it is found that it conducts search with only three leading wolves at the core, and balances the exploration and exploitation abilities by adjusting only the parameter a, which means the wolves lose some diversity to some extent. Therefore, a mutation operator is introduced to facilitate better searching wolves, and an eliminating- reconstructing mechanism is used for the poor search wolves, which not only effectively expands the stochastic search, but also accelerates its convergence, and these two operations complement each other well. To verify its validity, MR-GWO is applied to the global optimization experiment of 13 standard continuous functions and a radial basis function （RBF） network approximation experiment. Through a comparison with other algorithms, it is proven that MR-GWO has a strong advantage.

关键词： Swarm intelligence Grey wolf optimizer Optimization radial basis function network

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