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Future changes in rainfall, temperature and reference evapotranspiration in the central India by least square support vector machine 被引量:5
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作者 Sananda Kundu Deepak Khare Arun Mondal 《Geoscience Frontiers》 SCIE CAS CSCD 2017年第3期583-596,共14页
Climate change affects the environment and natural resources immensely. Rainfall, temperature and evapotranspiration are major parameters of climate affecting changes in the environment. Evapotrans- piration plays a k... Climate change affects the environment and natural resources immensely. Rainfall, temperature and evapotranspiration are major parameters of climate affecting changes in the environment. Evapotrans- piration plays a key role in crop production and water balance of a region, one of the major parameters affected by climate change. The reference evapotranspiration or ETo is a calculated parameter used in this research. In the present study, changes in the future rainfall, minimum and maximum temperature, and ETo have been shown by downscaling the HadCM3 (Hadley Centre Coupled Model version 3) model data. The selected study area is located in a part of the Narmada river basin area in Madhya Pradesh in central India. The downscaled outputs of projected rainfall, ETo and temperatures have been shown for the 21st century with the HADCM3 data of A2 scenario by the Least Square Support Vector Machine (LS-SVM) model. The efficiency of the LS-SVM model was measured by different statistical methods. The selected predictors show considerable correlation with the rainfall and temperature and the application of this model has been done in a basin area which is an agriculture based region and is sensitive to the change of rainfall and temperature. Results showed an increase in the future rainfall, temperatures and ETo. The temperature increase is projected in the high rise of minimum temperature in winter time and the highest increase in maximum temperature is projected in the pre-monsoon season or from March to May. Highest increase is projected in the 2080s in 2081-2091 and 2091-2099 in maximum temperature and 2091-2099 in minimum temperature in all the stations. Winter maximum temperature has been observed to have increased in the future. High rainfall is also observed with higher ETo in some decades. Two peaks of the increase are observed in ETo in the April-May and in the October. Variation in these parameters due to climate change might have an impact on the future water resource of the study area, which is mainly an agricultural based region, and will help in proper planning and management. 展开更多
关键词 Rainfall Temperature Reference evapotranspiration (ETo) Downscaling least square support vector machine (LS-SVM)
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Nonlinear correction of photoelectric displacement sensor based on least square support vector machine 被引量:1
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作者 郭杰荣 何怡刚 刘长青 《Journal of Central South University》 SCIE EI CAS 2011年第5期1614-1618,共5页
A model of correcting the nonlinear error of photoelectric displacement sensor was established based on the least square support vector machine.The parameters of the correcting nonlinear model,such as penalty factor a... A model of correcting the nonlinear error of photoelectric displacement sensor was established based on the least square support vector machine.The parameters of the correcting nonlinear model,such as penalty factor and kernel parameter,were optimized by chaos genetic algorithm.And the nonlinear correction of photoelectric displacement sensor based on least square support vector machine was applied.The application results reveal that error of photoelectric displacement sensor is less than 1.5%,which is rather satisfactory for nonlinear correction of photoelectric displacement sensor. 展开更多
关键词 least square support vector machine POSITION photoelectric displacement sensor nonlinear correct
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Least Squares One-Class Support Tensor Machine
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作者 Kaiwen Zhao Yali Fan 《Journal of Computer and Communications》 2024年第4期186-200,共15页
One-class classification problem has become a popular problem in many fields, with a wide range of applications in anomaly detection, fault diagnosis, and face recognition. We investigate the one-class classification ... One-class classification problem has become a popular problem in many fields, with a wide range of applications in anomaly detection, fault diagnosis, and face recognition. We investigate the one-class classification problem for second-order tensor data. Traditional vector-based one-class classification methods such as one-class support vector machine (OCSVM) and least squares one-class support vector machine (LSOCSVM) have limitations when tensor is used as input data, so we propose a new tensor one-class classification method, LSOCSTM, which directly uses tensor as input data. On one hand, using tensor as input data not only enables to classify tensor data, but also for vector data, classifying it after high dimensionalizing it into tensor still improves the classification accuracy and overcomes the over-fitting problem. On the other hand, different from one-class support tensor machine (OCSTM), we use squared loss instead of the original loss function so that we solve a series of linear equations instead of quadratic programming problems. Therefore, we use the distance to the hyperplane as a metric for classification, and the proposed method is more accurate and faster compared to existing methods. The experimental results show the high efficiency of the proposed method compared with several state-of-the-art methods. 展开更多
关键词 least square One-Class support Tensor machine One-Class Classification Upscale least square One-Class support vector machine One-Class support Tensor machine
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Least-Square Support Vector Machine and Wavelet Selection for Hearing Loss Identification 被引量:2
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作者 Chaosheng Tang Deepak Ranjan Nayak Shuihua Wang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2020年第10期299-313,共15页
Hearing loss(HL)is a kind of common illness,which can significantly reduce the quality of life.For example,HL often results in mishearing,misunderstanding,and communication problems.Therefore,it is necessary to provid... Hearing loss(HL)is a kind of common illness,which can significantly reduce the quality of life.For example,HL often results in mishearing,misunderstanding,and communication problems.Therefore,it is necessary to provide early diagnosis and timely treatment for HL.This study investigated the advantages and disadvantages of three classical machine learning methods:multilayer perceptron(MLP),support vector machine(SVM),and least-square support vector machine(LS-SVM)approach andmade a further optimization of the LS-SVM model via wavelet entropy.The investigation illustrated that themultilayer perceptron is a shallowneural network,while the least square support vector machine uses hinge loss function and least-square optimizationmethod.Besides,a wavelet selection method was proposed,and we found db4 can achieve the best results.The experiments showed that the LS-SVM method can identify the hearing loss disease with an overall accuracy of three classes as 84.89±1.77,which is superior to SVM andMLP.The results show that the least-square support vector machine is effective in hearing loss identification. 展开更多
关键词 Hearing loss wavelet entropy multilayer perceptron least square support vector machine
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A predictive model of chemical flooding for enhanced oil recovery purposes:Application of least square support vector machine 被引量:3
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作者 Mohammad Ali Ahmadi Maysam Pournik 《Petroleum》 2016年第2期177-182,共6页
Applying chemical flooding in petroleum reservoirs turns into interesting subject of the recent researches.Developing strategies of the aforementioned method are more robust and precise when they consider both economi... Applying chemical flooding in petroleum reservoirs turns into interesting subject of the recent researches.Developing strategies of the aforementioned method are more robust and precise when they consider both economical point of views(net present value(NPV))and technical point of views(recovery factor(RF)).In the present study huge attempts are made to propose predictive model for specifying efficiency of chemical flooding in oil reservoirs.To gain this goal,the new type of support vector machine method which evolved by Suykens and Vandewalle was employed.Also,high precise chemical flooding data banks reported in previous works were employed to test and validate the proposed vector machine model.According to the mean square error(MSE),correlation coefficient and average absolute relative deviation,the suggested LSSVM model has acceptable reliability;integrity and robustness.Thus,the proposed intelligent based model can be considered as an alternative model to monitor the efficiency of chemical flooding in oil reservoir when the required experimental data are not available or accessible. 展开更多
关键词 Chemical flooding Enhanced oil recovery(EOR) POLYMER SURFACTANT least square support vector machine (LSSVM)
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An Approximate Linear Solver in Least Square Support Vector Machine Using Randomized Singular Value Decomposition
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作者 LIU Bing XIANG Hua 《Wuhan University Journal of Natural Sciences》 CAS CSCD 2015年第4期283-290,共8页
In this paper, we investigate the linear solver in least square support vector machine(LSSVM) for large-scale data regression. The traditional methods using the direct solvers are costly. We know that the linear equ... In this paper, we investigate the linear solver in least square support vector machine(LSSVM) for large-scale data regression. The traditional methods using the direct solvers are costly. We know that the linear equations should be solved repeatedly for choosing appropriate parameters in LSSVM, so the key for speeding up LSSVM is to improve the method of solving the linear equations. We approximate large-scale kernel matrices and get the approximate solution of linear equations by using randomized singular value decomposition(randomized SVD). Some data sets coming from University of California Irvine machine learning repository are used to perform the experiments. We find LSSVM based on randomized SVD is more accurate and less time-consuming in the case of large number of variables than the method based on Nystrom method or Lanczos process. 展开更多
关键词 least square support vector machine Nystr?m method Lanczos process randomized singular value decomposition
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Image Denoising Using Local Adaptive Least Squares Support Vector Regression 被引量:7
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作者 WU Dingxue PENG Daiqiang TIAN Jinwen 《Geo-Spatial Information Science》 2007年第3期196-199,共4页
Rather than attempting to separate signal from noise in the spatial domain, it is often advantageous to work in a transform domain. Building on previous work, a novel denoising method based on local adaptive least squ... Rather than attempting to separate signal from noise in the spatial domain, it is often advantageous to work in a transform domain. Building on previous work, a novel denoising method based on local adaptive least squares support vector regression is proposed. Investigation on real images contaminated by Gaussian noise has demonstrated that the proposed method can achieve an acceptable trade off between the noise removal and smoothing of the edges and details. 展开更多
关键词 least square support vector machines image denoising
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Tribological properties and wear prediction model of TiC particles reinforced Ni-base alloy composite coatings 被引量:4
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作者 谭业发 何龙 +2 位作者 王小龙 洪翔 王伟刚 《Transactions of Nonferrous Metals Society of China》 SCIE EI CAS CSCD 2014年第8期2566-2573,共8页
TiC particles reinforced Ni-based alloy composite coatings were prepared on 7005 aluminum alloy by plasma spray. The effects of load, speed and temperature on the tribological behavior and mechanisms of the composite ... TiC particles reinforced Ni-based alloy composite coatings were prepared on 7005 aluminum alloy by plasma spray. The effects of load, speed and temperature on the tribological behavior and mechanisms of the composite coatings under dry friction were researched. The wear prediction model of the composite coatings was established based on the least square support vector machine (LS-SVM). The results show that the composite coatings exhibit smaller friction coefficients and wear losses than the Ni-based alloy coatings under different friction conditions. The predicting time of the LS-SVM model is only 12.93%of that of the BP-ANN model, and the predicting accuracies on friction coefficients and wear losses of the former are increased by 58.74%and 41.87%compared with the latter. The LS-SVM model can effectively predict the tribological behavior of the TiCP/Ni-base alloy composite coatings under dry friction. 展开更多
关键词 TiC particles Ni-based alloy composite coating least square support vector machine(LS-SVM) wear prediction model
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Intelligent control for large-scale variable speed variable pitch wind turbines 被引量:12
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作者 XinfangZHANG DapingXU YibingLIU 《控制理论与应用(英文版)》 EI 2004年第3期305-311,共7页
Large-scale wind turbine generator systems have strong nonlinear multivariable characteristics with many uncertain factors and disturbances. Automatic control is crucial for the efficiency and reliability of wind turb... Large-scale wind turbine generator systems have strong nonlinear multivariable characteristics with many uncertain factors and disturbances. Automatic control is crucial for the efficiency and reliability of wind turbines. On the basis of simplified and proper model of variable speed variable pitch wind turbines, the effective wind speed is estimated using extended Kaiman filter. Intelligent control schemes proposed in the paper include two loops which operate in synchronism with each other. At below-rated wind speed, the inner loop adopts adaptive fuzzy control based on variable universe for generator torque regulation to realize maximum wind energy capture. At above-rated wind speed, a controller based on least square support vector machine is proposed to adjust pitch angle and keep rated output power. The simulation shows the effectiveness of the intelligent control. 展开更多
关键词 Wind turbines Adaptive fuzzy control least square support vector machine Variable speed Variable pitch
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Regressive approach for predicting bearing capacity of bored piles from cone penetration test data 被引量:3
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作者 Iyad S. Alkroosh Mohammad Bahadori +1 位作者 Hamid Nikraz Alireza Bahadori 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2015年第5期584-592,共9页
In this study, th e least sq u are su p p o rt v ecto r m achine (LSSVM) alg o rith m w as applied to predicting th ebearing capacity o f b ored piles e m b ed d ed in sand an d m ixed soils. Pile g eo m etry an d c... In this study, th e least sq u are su p p o rt v ecto r m achine (LSSVM) alg o rith m w as applied to predicting th ebearing capacity o f b ored piles e m b ed d ed in sand an d m ixed soils. Pile g eo m etry an d cone p e n e tra tio nte s t (CPT) resu lts w ere used as in p u t variables for pred ictio n o f pile bearin g capacity. The d ata u se d w erecollected from th e existing litera tu re an d consisted o f 50 case records. The application o f LSSVM w ascarried o u t by dividing th e d ata into th re e se ts: a train in g se t for learning th e pro b lem an d obtain in g arelationship b e tw e e n in p u t variables an d pile bearin g capacity, and testin g an d validation sets forevaluation o f th e predictive an d g en eralization ability o f th e o b tain ed relationship. The predictions o f pilebearing capacity by LSSVM w ere evaluated by com paring w ith ex p erim en tal d ata an d w ith th o se bytrad itio n al CPT-based m eth o d s and th e gene ex pression pro g ram m in g (GEP) m odel. It w as found th a t th eLSSVM perform s w ell w ith coefficient o f d eterm in atio n , m ean, an d sta n d ard dev iatio n equivalent to 0.99,1.03, an d 0.08, respectively, for th e testin g set, an d 1, 1.04, an d 0.11, respectively, for th e v alidation set. Thelow values o f th e calculated m ean squared e rro r an d m ean ab so lu te e rro r indicated th a t th e LSSVM w asaccurate in p redicting th e pile bearing capacity. The results o f com parison also show ed th a t th e p roposedalg o rith m p red icted th e pile bearin g capacity m ore accurately th a n th e trad itio n al m eth o d s including th eGEP m odel. 展开更多
关键词 Bored piles Cone penetration test(CPT) Bearing capacity least square support vector machine(LSSVM) TRAINING VALIDATION
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An optimal method for prediction and adjustment on gasholder level and self-provided power plant gas supply in steel works 被引量:2
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作者 李红娟 王建军 +1 位作者 王华 孟华 《Journal of Central South University》 SCIE EI CAS 2014年第7期2779-2792,共14页
An optimal method for prediction and adjustment on byproduct gasholder level and self-provided power plant gas supply was proposed.This work raises the HP-ENN-LSSVM model based on the Hodrick-Prescott filter,Elman neu... An optimal method for prediction and adjustment on byproduct gasholder level and self-provided power plant gas supply was proposed.This work raises the HP-ENN-LSSVM model based on the Hodrick-Prescott filter,Elman neural network and least squares support vector machines.Then,according to the prediction,the optimal adjustment process came up by a novel reasoning method to sustain the gasholder within safety zone and the self-provided power plant boilers in economic operation,and prevent unfavorable byproduct gas emission and equipment trip as well.The experiments using the practical production data show that the proposed method achieves high accurate predictions and the optimal byproduct gas distribution,which provides a remarkable guidance for reasonable scheduling of byproduct gas. 展开更多
关键词 HP filter Elman neural network least square support vector machine gasholder level self-provided power plant
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Model Parameter Transfer for Gear Fault Diagnosis under Varying Working Conditions 被引量:2
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作者 Chao Chen Fei Shen +1 位作者 Jiawen Xu Ruqiang Yan 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2021年第1期168-180,共13页
Gear fault diagnosis technologies have received rapid development and been effectively implemented in many engineering applications.However,the various working conditions would degrade the diagnostic performance and m... Gear fault diagnosis technologies have received rapid development and been effectively implemented in many engineering applications.However,the various working conditions would degrade the diagnostic performance and make gear fault diagnosis(GFD)more and more challenging.In this paper,a novel model parameter transfer(NMPT)is proposed to boost the performance of GFD under varying working conditions.Based on the previous transfer strategy that controls empirical risk of source domain,this method further integrates the superiorities of multi-task learning with the idea of transfer learning(TL)to acquire transferable knowledge by minimizing the discrepancies of separating hyperplanes between one specific working condition(target domain)and another(source domain),and then transferring both commonality and specialty parameters over tasks to make use of source domain samples to assist target GFD task when sufficient labeled samples from target domain are unavailable.For NMPT implementation,insufficient target domain features and abundant source domain features with supervised information are fed into NMPT model to train a robust classifier for target GFD task.Related experiments prove that NMPT is expected to be a valuable technology to boost practical GFD performance under various working conditions.The proposed methods provides a transfer learning-based framework to handle the problem of insufficient training samples in target task caused by variable operation conditions. 展开更多
关键词 Gear fault diagnosis Model parameter transfer Varying working conditions least square support vector machine
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Determination of rock depth using artificial intelligence techniques 被引量:2
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作者 R.Viswanathan Pijush Samui 《Geoscience Frontiers》 SCIE CAS CSCD 2016年第1期61-66,共6页
This article adopts three artificial intelligence techniques, Gaussian Process Regression(GPR), Least Square Support Vector Machine(LSSVM) and Extreme Learning Machine(ELM), for prediction of rock depth(d) at ... This article adopts three artificial intelligence techniques, Gaussian Process Regression(GPR), Least Square Support Vector Machine(LSSVM) and Extreme Learning Machine(ELM), for prediction of rock depth(d) at any point in Chennai. GPR, ELM and LSSVM have been used as regression techniques.Latitude and longitude are also adopted as inputs of the GPR, ELM and LSSVM models. The performance of the ELM, GPR and LSSVM models has been compared. The developed ELM, GPR and LSSVM models produce spatial variability of rock depth and offer robust models for the prediction of rock depth. 展开更多
关键词 Rock depth Spatial variability least square support vector machine Gaussian process regression Extreme learning machine
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Combined forecast method of HMM and LS-SVM about electronic equipment state based on MAGA 被引量:1
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作者 Jianzhong Zhao Jianqiu Deng +1 位作者 Wen Ye Xiaofeng Lü 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2016年第3期730-738,共9页
For the deficiency that the traditional single forecast methods could not forecast electronic equipment states, a combined forecast method based on the hidden Markov model(HMM) and least square support vector machin... For the deficiency that the traditional single forecast methods could not forecast electronic equipment states, a combined forecast method based on the hidden Markov model(HMM) and least square support vector machine(LS-SVM) is presented. The multi-agent genetic algorithm(MAGA) is used to estimate parameters of HMM to overcome the problem that the Baum-Welch algorithm is easy to fall into local optimal solution. The state condition probability is introduced into the HMM modeling process to reduce the effect of uncertain factors. MAGA is used to estimate parameters of LS-SVM. Moreover, pruning algorithms are used to estimate parameters to get the sparse approximation of LS-SVM so as to increase the ranging performance. On the basis of these, the combined forecast model of electronic equipment states is established. The example results show the superiority of the combined forecast model in terms of forecast precision,calculation speed and stability. 展开更多
关键词 parameter estimation hidden Markov model(HMM) least square support vector machine(LS-SVM) multi-agent genetic algorithm(MAGA) state forecast
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Improved Model of Radial Vibration in Switched Reluctance Motor Including Magnetic Saturation 被引量:2
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作者 Xiaoqiang Guo Rui Zhong +2 位作者 Mingshu Zhang Desheng Ding Weifeng Sun 《CES Transactions on Electrical Machines and Systems》 2018年第4期363-370,共8页
This paper proposes an improved method for the prediction of radial vibration in switched reluctance motor(SRM)considering magnetic saturation.In this paper,the basic modeling principle is briefly introduced,it is bas... This paper proposes an improved method for the prediction of radial vibration in switched reluctance motor(SRM)considering magnetic saturation.In this paper,the basic modeling principle is briefly introduced,it is based on the derivation that the peak acceleration is dependent on the product of phase current and current gradient idi/dt.However,the derivation may cause errors due to saturation effect.Thus in this paper,the discrete sample data are firstly acquired based on DC pulse measurement method,by which electromagnetic,torque and peak acceleration characteristics can all be acquired.Then the entire peak acceleration characteristics are obtained by improved Least Square Support Vector Machine(LSSVM).Based on the obtained static peak acceleration characteristics,the time-varied radial vibration model is established based on superposition of natural oscillations of dominant vibration modes.Finally,a simulation model is built up using MATLAB/Simulink.The good agreement between simulation and experiment shows that the proposed method for modeling is feasible and accurate,even under saturation.In addition,since LSSVM does not need any prior knowledge,it is much easier for modeling compared with other existing literatures. 展开更多
关键词 Acoustic noise magnetic saturation least square support vector machine LSSVM MIMO MODELING radial vibration switched reluctance motor
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Soft measurement model on torque of alternating current electrical dynamometer including copper loss and iron loss
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作者 ZHONG Ding-qing WANG Ai-lun HE Qian 《Journal of Central South University》 SCIE EI CAS CSCD 2019年第8期2272-2280,共9页
Alternating current electrical dynamometer is a common device to measure the torque of engines, such as the gasoline engine. In order to solve the problems such as high cost, high energy consumption and complicated me... Alternating current electrical dynamometer is a common device to measure the torque of engines, such as the gasoline engine. In order to solve the problems such as high cost, high energy consumption and complicated measurement system which exists in the direct measurement on the torque of alternating current electrical dynamometer, copper loss and iron loss are taken as two key factors and a soft-sensing model on the torque of alternating current electrical dynamometer is established using the fuzzy least square support vector machine (FLS-SVM). Then, the FLS-SVM parameters such as penalty factor and kernel parameter are optimized by adaptive genetic algorithm, torque soft-sensing is investigated in the alternating current electrical dynamometer, as well as the energy feedback efficiency and energy consumption during the measurement phase of a gasoline engine loading continual test is obtained. The results show that the minimum soft-sensing error of torque is about 0.0018, and it fluctuates within a range from -0.3 to 0.3 N·m. FLS-SVM soft-sensing method can increase by 1.6% power generation feedback compared with direct measurement, and it can save 500 kJ fuel consumption in the gasoline engine loading continual test. Therefore, the estimation accuracy of the soft measurement model on the torque of alternating current electrical dynamometer including copper loss and iron loss is high and this indirect measurement method can be feasible to reduce production cost of the alternating current electrical dynamometer and energy consumption during the torque measurement phase of a gasoline engine, replacing the direct method of torque measurement. 展开更多
关键词 TORQUE fuzzy theory least square support vector machine alternating current electrical dynamometer
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LSSVM Predictive Control for Calcination Zone Temperature in Rotary Kiln with IHS Algorithm
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作者 Zhongda Tian Shujiang Li +1 位作者 Yanhong Wang Xiangdong Wang 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2016年第4期67-74,共8页
The calcination zone temperature control is an important problem in rotary kiln production process. In order to solve this problem,a predictive control method based on improved harmony search algorithm( IHS)and least ... The calcination zone temperature control is an important problem in rotary kiln production process. In order to solve this problem,a predictive control method based on improved harmony search algorithm( IHS)and least square support vector machine( LSSVM) is proposed. LSSVM is utilized to bulid the nonlinear predictive model of calcination zone temperature in rotary kiln. The calcination zone temperature can be predicted through input control variable,the error and error correction of output feedback. The performance index function is established by deviation and control variable. An IHS algorithm with better fitness and faster convergence speed is proposed. The optimal control variable can be obtained by rolling optimization through this IHS algorithm. The stability of this predictive control method is proved to be feasible. The simulation and actual experiment results show that the proposed predictive control method has good control performance. 展开更多
关键词 rotary kiln calcination zone temperature least square support vector machine improved harmony search predictive control
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A NOVEL INDOOR GEO-LOCATION METHOD USING MIMO ARRAY
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作者 Sun Guolin Guo Wei 《Journal of Electronics(China)》 2006年第6期810-813,共4页
In a Multiple-Input Multiple-Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) based Wireless Local Area Network (WLAN) system, both Access Points (APs) and Mobile Termi-nals (MTs) are configured with mu... In a Multiple-Input Multiple-Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) based Wireless Local Area Network (WLAN) system, both Access Points (APs) and Mobile Termi-nals (MTs) are configured with multiple antennas, to make novel indoor geo-location method possible. In this paper, we presented a novel Least Square Support Vector Machine (LS-SVM) based data fusion algorithm to fuse signal strength measurements for indoor geo-location using only a single AP with MIMO arrays. We evaluate our proposed algorithms under indoor environments by MATLAB simulations. Simulation results show that our MIMO-based algorithm is superior to conventional least square algorithm. 展开更多
关键词 Indoor geo-location Multiple-Input Multiple-Output (MIMO) least square support vector machine (LS-SVM) Wireless Local Area Network (WLAN)
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Fault Diagnosis of Overflow Valve Based on Trispectrum
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作者 Wenbing Wu 《World Journal of Engineering and Technology》 2020年第4期765-773,共9页
The high-order spectrum can effectively remove Gaussian noise. The three-spectrum and its slices represent random signals from a higher probability structure. It can not only qualitatively describe the linearity and n... The high-order spectrum can effectively remove Gaussian noise. The three-spectrum and its slices represent random signals from a higher probability structure. It can not only qualitatively describe the linearity and nonlinearity of vibration signals closely related to mechanical failures, Gaussian and non-Gaussian Performance, and can greatly i</span><span style="font-family:Verdana;"></span><span style="font-family:"">mprove the accuracy of mechanical fault diagnosis. The two-dimensional slices of trispectrum in normal and fault states show different peak characteristics. 2-D wavelet multi-level decomposition can effectively compress 2-D array information. Least squares support vector machine can obtain the global optimum under limited samples, thus avoiding the local optimum problem, and has the advantage of reducing computational complexity. In this paper, 2-D wavelet multi-level decomposition is used to extract features of trispectrum 2-D slices, and input LSSVM to diagnose the fault of the pressure reducing valve, which has achieved good results. 展开更多
关键词 Speed Control Valve Trispectrum Two-Dimensional Slice Two-Dimensional Wavelet least square support vector machine Fault Diagnosis
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