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Feature Selection by Merging Sequential Bidirectional Search into relevance vector machine in Condition Monitoring

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Chinese Journal of Mechanical Engineering 2015年第6期28卷 1248-1253页

作者： ZHANG Kui DONG Yu BALL Andrew School of Economics & Management Wuhan Polytechnic University School of Information Engineering Communication University of China School of Computing & Engineering University of Huddersfield

For more accurate fault detection and diagnosis, there is an increasing trend to use a large number of sensors and to collect data at high frequency. This inevitably produces large-scale data and causes difficulties in fault classification. Actually, the classification methods are simply intractable when applied to high-dimensional condition monitoring data. In order to solve the problem, engineers have to resort to complicated feature extraction methods to reduce the dimensionality of data. However, the features transformed by the methods cannot be understood by the engineers due to a loss of the original engineering meaning. In this paper, other forms of dimensionality reduction technique（feature selection methods） are employed to identify machinery condition, based only on frequency spectrum data. Feature selection methods are usually divided into three main types： filter, wrapper and embedded methods. Most studies are mainly focused on the first two types, whilst the development and application of the embedded feature selection methods are very limited. This paper attempts to explore a novel embedded method. The method is formed by merging a sequential bidirectional search algorithm into scale parameters tuning within a kernel function in the relevance vector machine. To demonstrate the potential for applying the method to machinery fault diagnosis, the method is implemented to rolling bearing experimental data. The results obtained by using the method are consistent with the theoretical interpretation, proving that this algorithm has important engineering significance in revealing the correlation between the faults and relevant frequency features. The proposed method is a theoretical extension of relevance vector machine, and provides an effective solution to detect the fault-related frequency components with high efficiency.

关键词： feature selection relevance vector machine sequential bidirectional search fault diagnosis

Data-driven optimal operation of the industrial methanol to olefin process based on relevance vector machine

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Chinese Journal of Chemical Engineering 2021年第6期34卷 106-115页

作者： Zhiquan Wang Liang Wang Zhihong Yuan Bingzhen Chen Department of Chemical Engineering Tsinghua UniversityBeijing 100084China State Key Laboratory of Chemical Engineering Department of Chemical EngineeringTsinghua UniversityBeijing 100084China

Methanol to olefin(MTO)technology provides the opportunity to produce olefins from nonpetroleum sources such as coal,biomass and natural *** than 20 commercial MTO plants have been put into *** now,contributions on optimal operation of industrial MTO plants from a process systems engineering perspective are *** on relevance vector machine(RVM),a data-driven framework for optimal operation of the industrial MTO process is established to fully utilize the plentiful industrial data *** correlates the yield distribution prediction of main products and the operation *** correlations then serve as the constraints for the multi-objective optimization model to pursue the optimal operation of the *** sorting genetic algorithmⅡis used to solve the optimization *** tests demonstrate that the ethylene yield is effectively improved based on the proposed *** RVM does provide the distribution prediction instead of point estimation,the established model is expected to provide guidance for actual production operations under uncertainty.

关键词： Methanol to olefins relevance vector machine Genetic algorithm Operation optimization Systems engineering Process systems

Seismic fragility analysis of bridges by relevance vector machine based demand prediction model

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Earthquake Engineering and Engineering Vibration 2022年第1期21卷 253-268页

作者： Swarup Ghosh Subrata Chakraborty Department of Civil Engineering Indian Institute of Engineering Science and TechnologyShibpurHowrah 711103India

A relevance vector machine(RVM)based demand prediction model is explored for efficient seismic fragility analysis(SFA)of a bridge *** proposed RVM model integrates both record-to-record variations of ground motions and uncertainties of parameters characterizing the bridge *** efficient fragility computation,ground motion intensity is included as an added dimension to the demand prediction *** incorporate different sources of uncertainty,random realizations of different structural parameters are generated using Latin hypercube sampling *** fragility,along with its dispersions,is estimated based on the log-normal fragility model for different critical components of a *** effectiveness of the proposed RVM model-based SFA of a bridge structure is elucidated numerically by comparing it with fragility results obtained by the commonly used SFA approaches,while considering the most accurate direct Monte Carlo simulation-based fragility estimates as the *** proposed RVM model provides a more accurate estimate of fragility than conventional approaches,with significantly less computational *** addition,the proposed model provides a measure of uncertainty in fragility estimates by constructing confidence intervals for the fragility curves.

关键词： bridge structure seismic fragility analysis seismic demand model relevance vector machine

Seismic liquefaction potential assessment by using relevance vector machine

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Earthquake Engineering and Engineering Vibration 2007年第4期6卷 331-336页

作者： Pijush Samui Department of Civil Engineering Indian Institute of Science Bangalore 560012 India

Determining the liquefaction potential of soil is important in earthquake engineering. This study proposes the use of the relevance vector machine （RVM） to determine the liquefaction potential of soil by using actual cone penetration test （CPT） data. RVM is based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. The results are compared with a widely used artificial neural network （ANN） model. Overall, the RVM shows good performance and is proven to be more accurate than the ANN model. It also provides probabilistic output. The model provides a viable tool for earthquake engineers to assess seismic conditions for sites that are susceptible to liquefaction.

关键词： liquefaction cone penetration test relevance vector machine artificial neural network

Prediction of Compressive Strength of Various SCC Mixes Using relevance vector machine

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Computers, Materials & Continua 2018年第1期54卷 83-102页

作者： G.Jayaprakash M.P.Muthuraj Assistant Professor Dept.Civil Engg.KGISL Institute of TechnologyCoimbatore-641035India Assistant Professor Dept.Civil Engg.Coimbatore Institute of TechnologyCoimbatore-641014 India

This paper discusses the applicability of relevance vector machine(RVM)based regression to predict the compressive strength of various self compacting concrete(SCC)*** strength data various SCC mixes has been consolidated by considering the effect of water cement ratio,water binder ratio and steel *** vector machine(RVM)is a machine learning technique that uses Bayesian inference to obtain parsimonious solutions for regression and *** RVM has an identical functional form to the support vector machine,but provides probabilistic classification and *** is based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse *** strength model has been developed by using MATLAB software for training and *** 75%of the data has been used for development of model and 30%of the data is used for *** predicted compressive strength for SCC mixes is found to be in very good agreement with those of the corresponding experimental observations available in the literature.

关键词： relevance vector machine Self-compacting concrete Compressive strength Variance

On Guidance Parameter Calculation of Flight Vehicle Using relevance vector machine and Particle Swarm Optimization

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On Guidance Parameter Calculation of Flight Vehicle Using Re...

Study on Classification Model Based on relevance vector machine with Genetic Algorithm

第三十九届中国控制会议

作者： Yao Zhao Yun Tan Hai-tao Tian Ying Zou Beijing Aerospace Automatic Control Institute

To deal with the problem that guidance parameter calculation of flight vehicle requires a lot of computer memory space to store interpolation data tables, a novel guidance parameter calculation method based on relevance vector machine（RVM） and particle swarm optimization（PSO） is presented. By utilizing non-linear mapping property of RVM, parameter regression model for calculating guidance parameter is constructed. The model is trained based on the samples which are generated from the interpolation data tables of flight control trajectories. During training, the Gaussian kernel parameter of RVM is automatically selected by the use of PSO. Simulations on non-linear function approximation and flight vehicle data show that PSO-RVM model achieves high regression accuracy and the structure of model is much sparser. The guidance parameter can be calculated by storing less parameter in the controller of flight vehicle, and the presented method has some engineering application value.

关键词： Guidance parameter calculation flight vehicle relevance vector machine particle swarm optimization

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Study on Classification Model Based on Relevance Vector Mach...

An Integrated Method for Lithium-ion Batteries Remaining Useful Life Prediction Based on Unscented Particle Filter and relevance vector machine

2010 2nd IEEE International Conference on Information and Financial Engineering(ICIFE2010)

作者： Yanhong Li Mathematical College Beihua University Jilin 132013 China Taihui Liu Computer College Beihua University Jilin 132013 China

A novel classification method based on relevance vector machine with genetic algorithm is presented in the *** the model,genetic algorithm is applied to gain the suitable training parameters of relevance vector *** classification of roll bearing is applied to testify the classification ability of the proposed method,and state classification data of roll bearing are *** experimental results show that relevance vector machine with genetic algorithm has higher classification accuracy than backpropagation neural network and support vector machine.

关键词： relevance vector machine classification model genetic algorithm Bayesian

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An Integrated Method for Lithium-ion Batteries Remaining Use...

A new support vector machine based multiuser detection scheme

第40届中国控制会议

作者： Meng Jiang Zhenxing Liu Yong Zhang Jie He Yujie Chen School of Information Science and Engineering Wuhan University of Science and Technology Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education

Accurate remaining useful life（RUL） prediction for lithium-ion batteries plays a crucial role to prevent accidents from happening. In this paper, we proposed an hybrid method based on unscented particle filter（UPF） and relevance vector machine（RVM）, where the genetic algorithm optimized support vector regression（GA-SVR） was uesd to correct the error generated in the prediction process. RVM is first used to extract feature points and initialize degradation model. In order to implement the RUL prediction, UPF is employed to extrapolate trends of the battery degradation capacity, where the unscented Kalman filter（UKF）is applied to generate a proposal distribution as an importance function for PF. In addition, GA-SVR is uesd to correct prediction error. Finally, to verify the effect of the proposed UPF-GASVR method with higher prediction accuracy, NASA battery dataset was used to carry out the test and compare with some classical methods.

关键词： Remaining useful life prediction Unscented particle filter relevance vector machine Genetic algorithm Support vector regression

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Journal of Harbin Institute of Technology(New Series) 2008年第5期15卷 620-623页

作者：王永建赵洪林 The Research Center on Communication Harbin Institute of Technology

In order to suppress the multiple access interference (MAI) in 3G, which limits the capacity of a CDMA communication system, a fast relevance vector machine (FRVM) is employed in the multiuser detection (MUD) scheme. This method aims to overcome the shortcomings of many ordinary support vector machine (SVM) based MUD schemes, such as the long training time and the inaccuracy of the decision data, and enhance the performance of a CDMA communication system. Computer simulation results demonstrate that the proposed FRVM based multiuser detection has lower bit error rate, costs short training time, needs fewer kernel functions and possesses better near-far resistance.

关键词： multiuser detection support vector machine relevance vector machine bit error rate