The theoretical studies of bionics of machinery have great scientific significance, and the development of bionic machines has large practical values in the field of engineering and technology. Through the rigorous se...The theoretical studies of bionics of machinery have great scientific significance, and the development of bionic machines has large practical values in the field of engineering and technology. Through the rigorous selection process of evolution, the survived living organisms have successfully developed outstanding abilities to adapt to their surroundings and to reproduce their offspring. In this review,we interpreted the fundamental principles of anti-adhesion and anti-resistance of soil animals by reviewing the current status in this research field and summarizing the work of the research group at Jilin University of China in the past decades. The principles and technologies used in morphology bionics,electric-osmosis bionics,flexibility bionics,configuration bionics and coupling bionics were examined.Finally,the applications of the engineering bionics and their extensive prospects were introduced.展开更多
This study aims to develop several optimization techniques for predicting advance rate of tunnel boring machine(TBM)in different weathered zones of granite.For this purpose,extensive field and laboratory studies have ...This study aims to develop several optimization techniques for predicting advance rate of tunnel boring machine(TBM)in different weathered zones of granite.For this purpose,extensive field and laboratory studies have been conducted along the 12,649 m of the Pahang-Selangor raw water transfer tunnel in Malaysia.Rock properties consisting of uniaxial compressive strength(UCS),Brazilian tensile strength(BTS),rock mass rating(RMR),rock quality designation(RQD),quartz content(q)and weathered zone as well as machine specifications including thrust force and revolution per minute(RPM)were measured to establish comprehensive datasets for optimization.Accordingly,to estimate the advance rate of TBM,two new hybrid optimization techniques,i.e.an artificial neural network(ANN)combined with both imperialist competitive algorithm(ICA)and particle swarm optimization(PSO),were developed for mechanical tunneling in granitic rocks.Further,the new hybrid optimization techniques were compared and the best one was chosen among them to be used for practice.To evaluate the accuracy of the proposed models for both testing and training datasets,various statistical indices including coefficient of determination(R^2),root mean square error(RMSE)and variance account for(VAF)were utilized herein.The values of R^2,RMSE,and VAF ranged in 0.939-0.961,0.022-0.036,and 93.899-96.145,respectively,with the PSO-ANN hybrid technique demonstrating the best performance.It is concluded that both the optimization techniques,i.e.PSO-ANN and ICA-ANN,could be utilized for predicting the advance rate of TBMs;however,the PSO-ANN technique is superior.展开更多
The support vector machine （SVM） is a novel machine learning method, which has the ability to approximate nonlinear functions with arbitrary accuracy. Setting parameters well is very crucial for SVM learning results...The support vector machine （SVM） is a novel machine learning method, which has the ability to approximate nonlinear functions with arbitrary accuracy. Setting parameters well is very crucial for SVM learning results and generalization ability, and now there is no systematic, general method for parameter selection. In this article, the SVM parameter selection for function approximation is regarded as a compound optimization problem and a mutative scale chaos optimization algorithm is employed to search for optimal paraxneter values. The chaos optimization algorithm is an effective way for global optimal and the mutative scale chaos algorithm could improve the search efficiency and accuracy. Several simulation examples show the sensitivity of the SVM parameters and demonstrate the superiority of this proposed method for nonlinear function approximation.展开更多
A new method based on principal component analysis (PCA) and support vector machines (SVMs) is proposed for fault diagnosis of mine hoists. PCA is used to extract the principal features associated with the gearbox. Th...A new method based on principal component analysis (PCA) and support vector machines (SVMs) is proposed for fault diagnosis of mine hoists. PCA is used to extract the principal features associated with the gearbox. Then, with the irrelevant gearbox variables removed, the remaining gearbox, the hydraulic system and the wire rope parameters were used as input to a multi-class SVM. The SVM is first trained by using the one class-based multi-class optimization algorithm and it is then applied to fault identification. Comparison of various methods showed the PCA-SVM method successfully removed redundancy to solve the dimensionality curse. These results show that the algorithm using the RBF kernel function for the SVM had the best classification properties.展开更多
A study of dimensional synthesis of a 3-DOF parallel manipulator with coupling of translation and rotation is carried out.The architecture of the manipulator is composed of a moving platform attached to a fixed base t...A study of dimensional synthesis of a 3-DOF parallel manipulator with coupling of translation and rotation is carried out.The architecture of the manipulator is composed of a moving platform attached to a fixed base through three identical PRS(prismatic-revolute-spherical) serial limbs,whose unique topology leads to the physical unit inconsistency of the conventional Jacobian matrix and the emergence of the parasitic motion.Then this paper introduces a kinetostatic performance index of the manipulator based on the condition of a dimensionally homogeneous Jacobian matrix,later,the workspace of the aforementioned manipulator is searched and the influence of the crucial design variables on the workspace is analyzed.Finally,a dimensional synthesis method of the manipulator is proposed,which may be regarded as a nonlinear programming problem with subject to the parasitic motion and other several engineering constraints.展开更多
Aiming at the problems of the traditional method of assessing distribution of particle size in bench blasting, a support vector machines （SVMs） regression methodology was used to predict the mean particle size （X50...Aiming at the problems of the traditional method of assessing distribution of particle size in bench blasting, a support vector machines （SVMs） regression methodology was used to predict the mean particle size （X50） resulting from rock blast fragmentation in various mines based on the statistical learning theory. The data base consisted of blast design parameters, explosive parameters, modulus of elasticity and in-situ block size. The seven input independent variables used for the SVMs model for the prediction of X50 of rock blast fragmentation were the ratio of bench height to drilled burden （H/B）, ratio of spacing to burden （S/B）, ratio of burden to hole diameter （B/D）, ratio of stemming to burden （T/B）, powder factor （Pf）, modulus of elasticity （E） and in-situ block size （XB）. After using the 90 sets of the measured data in various mines and rock formations in the world for training and testing, the model was applied to 12 another blast data for validation of the trained support vector regression （SVR） model. The prediction results of SVR were compared with those of artificial neural network （ANN）, multivariate regression analysis （MVRA） models, conventional Kuznetsov method and the measured X50 values. The proposed method shows promising results and the prediction accuracy of SVMs model is acceptable.展开更多
Parallel kinematic machines （PKMs） have the advantages of a compact structure,high stiffness,a low moving inertia,and a high load/weight ratio.PKMs have been intensively studied since the 1980s,and are still attract...Parallel kinematic machines （PKMs） have the advantages of a compact structure,high stiffness,a low moving inertia,and a high load/weight ratio.PKMs have been intensively studied since the 1980s,and are still attracting much attention.Compared with extensive researches focus on their type/dimensional synthesis,kinematic/dynamic analyses,the error modeling and separation issues in PKMs are not studied adequately,which is one of the most important obstacles in its commercial applications widely.Taking a 3-PRS parallel manipulator as an example,this paper presents a separation method of source errors for 3-DOF parallel manipulator into the compensable and non-compensable errors effectively.The kinematic analysis of 3-PRS parallel manipulator leads to its six-dimension Jacobian matrix,which can be mapped into the Jacobian matrix of actuations and constraints,and then the compensable and non-compensable errors can be separated accordingly.The compensable errors can be compensated by the kinematic calibration,while the non-compensable errors may be adjusted by the manufacturing and assembling process.Followed by the influence of the latter,i.e.,the non-compensable errors,on the pose error of the moving platform through the sensitivity analysis with the aid of the Monte-Carlo method,meanwhile,the configurations of the manipulator are sought as the pose errors of the moving platform approaching their maximum.The compensable and non-compensable errors in limited-DOF parallel manipulators can be separated effectively by means of the Jacobian matrix of actuations and constraints,providing designers with an informative guideline to taking proper measures for enhancing the pose accuracy via component tolerancing and/or kinematic calibration,which can lay the foundation for the error distinguishment and compensation.展开更多
The purpose of this study is to apply some statistical and soft computing methods such as Fisher discriminant analysis （FDA） and support vector machines （SVMs） methodology to the determination of pillar stability ...The purpose of this study is to apply some statistical and soft computing methods such as Fisher discriminant analysis （FDA） and support vector machines （SVMs） methodology to the determination of pillar stability for underground mines selected from various coal and stone mines by using some index and mechanical properties, including the width, the height, the ratio of the pillar width to its height, the uniaxial compressive strength of the rock and pillar stress. The study includes four main stages： sampling, testing, modeling and assessment of the model performances. During the modeling stage, two pillar stability prediction models were investigated with FDA and SVMs methodology based on the statistical learning theory. After using 40 sets of measured data in various mines in the world for training and testing, the model was applied to other 6 data for validating the trained proposed models. The prediction results of SVMs were compared with those of FDA as well as the measured field values. The general performance of models developed in this study is close; however, the SVMs exhibit the best performance considering the performance index with the correct classification rate Prs by re-substitution method and Pcv by cross validation method. The results show that the SVMs approach has the potential to be a reliable and practical tool for determination of pillar stability for underground mines.展开更多
With the applications of high technology,a catastrophic failure of CNC equipment rarely occurs at normal operation conditions.So it is difficult for traditional reliability assessment methods based on time-to-failure ...With the applications of high technology,a catastrophic failure of CNC equipment rarely occurs at normal operation conditions.So it is difficult for traditional reliability assessment methods based on time-to-failure distributions to deduce the reliability level.This paper presents a novel reliability assessment methodology to estimate the reliability level of equipment with machining performance degradation data when only a few samples are available.The least squares support vector machines(LS-SVM) are introduced to analyze the performance degradation process on the equipment.A two-stage parameter optimization and searching method is proposed to improve the LS-SVM regression performance and a reliability assessment model based on the LS-SVM is built.A machining performance degradation experiment has been carried out on an OTM650 machine tool to validate the effectiveness of the proposed reliability assessment methodology.展开更多
Particle Swarm Optimization （PSO） is a popular and bionic algorithm based on the social behavior associated with bird flocking for optimization problems. To maintain the diversity of swarms, a few studies of multi-s...Particle Swarm Optimization （PSO） is a popular and bionic algorithm based on the social behavior associated with bird flocking for optimization problems. To maintain the diversity of swarms, a few studies of multi-swarm strategy have been reported. However, the competition among swarms, reservation or destruction of a swarm, has not been considered further. In this paper, we formulate four rules by introducing the mechanism for survival of the fittest, which simulates the competition among the swarms. Based on the mechanism, we design a modified Multi-Swarm PSO （MSPSO） to solve discrete problems, which consists of a number of sub-swarms and a multi-swarm scheduler that can monitor and control each sub-swarm using the rules. To further settle the feature selection problems, we propose an Improved Feature Selection （1FS） method by integrating MSPSO, Support Vector Machines （SVM） with F-score method. The IFS method aims to achieve higher generalization capa- bility through performing kernel parameter optimization and feature selection simultaneously. The performance of the proposed method is compared with that of the standard PSO based, Genetic Algorithm （GA） based and the grid search based mcthods on 10 benchmark datasets, taken from UCI machine learning and StatLog databases. The numerical results and statistical analysis show that the proposed IFS method performs significantly better than the other three methods in terms of prediction accuracy with smaller subset of features.展开更多
The robust parameter design method is a traditional approach to robust experimental design that seeks to obtain the optimal combination of factors/levels. To overcome some of the defects of the inflatable wing paramet...The robust parameter design method is a traditional approach to robust experimental design that seeks to obtain the optimal combination of factors/levels. To overcome some of the defects of the inflatable wing parameter design method, this paper proposes an optimization design scheme based on orthogonal testing and support vector machines （SVMs）. Orthogonal testing design is used to estimate the appropriate initial value and variation domain of each variable to decrease the number of iterations and improve the identification accuracy and efficiency. Orthogonal tests consisting of three factors and three levels are designed to analyze the parameters of pressure, uniform applied load and the number of chambers that affect the bending response of inflatable wings. An SVM intelligent model is established and limited orthogonal test swatches are studied. Thus, the precise relationships between each parameter and product quality features, as well the signal-to-noise ratio （SNR）, can be obtained. This can guide general technological design optimization.展开更多
In this article, a nonlinear dynamic multiway partial least squares (MPLS) based on support vector machines (SVM) is developed for on-line fault detection in batch processes. The approach, referred to as SVM-based DMP...In this article, a nonlinear dynamic multiway partial least squares (MPLS) based on support vector machines (SVM) is developed for on-line fault detection in batch processes. The approach, referred to as SVM-based DMPLS, integrates the SVM with the MPLS model. Process data from normal historical batches are used to develop the MPLS model, and a series of single-input-single-output SVM networks are adopted to approximate nonlinear inner relationship between input and output variables. In addition, the application of a time-lagged window technique not only makes the complementarities of unmeasured data of the monitored batch unnecessary, but also significantly reduces the computation and storage requirements in comparison with the traditional MPLS. The proposed approach is validated by a simulation study of on-line fault detection for a fed-batch penicillin production.展开更多
Due to the limited transmission resources for data relay in the tracking and data relay satellite system （TDRSS）, there are many job requirements in busy days which will be discarded in the conventional job scheduli...Due to the limited transmission resources for data relay in the tracking and data relay satellite system （TDRSS）, there are many job requirements in busy days which will be discarded in the conventional job scheduling model. Therefore, the improvement of scheduling efficiency in the TDRSS can not only help to increase the resource utilities, but also to reduce the scheduling failure ratio. A model of nonhomogeneous parallel machines scheduling problems with time window （NPM-TW） is firstly built up for the TDRSS, considering the distinct features of the variable preparation time and the nonhomogeneous transmission rates for different types of antennas on each tracking and data relay satellite （TDRS）. Then, an adaptive subsequence adjustment （ASA） framework with evolutionary asymmetric path-relinking （EvAPR） is proposed to solve this problem, in which an asymmetric progressive crossover operation is involved to overcome the local optima by the conventional job inserting methods. The numerical results show that, compared with the classical greedy randomized adaptive search procedure （GRASP） algorithm, the scheduling failure ratio of jobs can be reduced over 11% on average by the proposed ASA with EvAPR.展开更多
Sensitivity analysis of hydrological model is the key for model uncertainty quantification. However, how to effectively validate model and identify the dominant parameters for distributed hydrological models is a bott...Sensitivity analysis of hydrological model is the key for model uncertainty quantification. However, how to effectively validate model and identify the dominant parameters for distributed hydrological models is a bottle-neck to achieve parameters optimization. For this reason, a new approach was proposed in this paper, in which the support vector machine was used to construct the response surface at first. Then it integrates the SVM-based response surface with the Sobol＇ method, i.e. the RSMSoboI＇ method, to quantify the parameter sensi- tivities. In this work, the distributed time-variant gain model （DTVGM） was applied to the Huaihe River Basin, which was used as a case to verify its validity and feasibility. We selected three objective functions （i.e. water balance coefficient WB, Nash-Sutcliffe efficiency coefficient NS, and correlation coefficient RC） to assess the model performance as the output responses for sensitivity analysis. The results show that the parameters gl and g2 are most important for all the objective functions, and they are almost the same to that of the classical approach. Furthermore, the RSMSobol method can not only achieve the quantification of the sensitivity, and also reduce the computational cost, with good accuracy compared to the classical approach. And this approach will be effective and reliable in the global sensitivity analysis for a complex modelling system.展开更多
Hyperspectral reflectance (350-2500 nm) measurements were made over two experimental rice fields containing two cultivars treated with three levels of nitrogen application.Four different transformations of the reflect...Hyperspectral reflectance (350-2500 nm) measurements were made over two experimental rice fields containing two cultivars treated with three levels of nitrogen application.Four different transformations of the reflectance data were analyzed for their capability to predict rice biophysical parameters,comprising leaf area index (LAI;m-2 green leaf area m-2 soil) and green leaf chlorophyll density (GLCD;mg chlorophyll m 2 soil),using stepwise multiple regression (SMR) models and support vector machines (SVMs).Four transformations of the rice canopy data were made,comprising reflectances (R),first-order derivative reflectances (D1),second-order derivative reflectances (D2),and logarithm transformation of reflectances (LOG).The polynomial kernel (POLY) of the SVM using R was the best model to predict rice LAI,with a root mean square error (RMSE) of 1.0496 LAI units.The analysis of variance kernel of SVM using LOG was the best model to predict rice GLCD,with an RMSE of 523.0741 mg m-2.The SVM approach was not only superior to SMR models for predicting the rice biophysical parameters,but also provided a useful exploratory and predictive tool for analyzing different transformations of reflectance data.展开更多
Support vector machines and a Kalman-like observer are used for fault detection and isolation in a variable speed horizontalaxis wind turbine composed of three blades and a full converter. The support vector approach ...Support vector machines and a Kalman-like observer are used for fault detection and isolation in a variable speed horizontalaxis wind turbine composed of three blades and a full converter. The support vector approach is data-based and is therefore robust to process knowledge. It is based on structural risk minimization which enhances generalization even with small training data set and it allows for process nonlinearity by using flexible kernels. In this work, a radial basis function is used as the kernel. Different parts of the process are investigated including actuators and sensors faults. With duplicated sensors, sensor faults in blade pitch positions,generator and rotor speeds can be detected. Faults of type stuck measurements can be detected in 2 sampling periods. The detection time of offset/scaled measurements depends on the severity of the fault and on the process dynamics when the fault occurs. The converter torque actuator fault can be detected within 2 sampling periods. Faults in the actuators of the pitch systems represents a higher difficulty for fault detection which is due to the fact that such faults only affect the transitory state(which is very fast) but not the final stationary state. Therefore, two methods are considered and compared for fault detection and isolation of this fault: support vector machines and a Kalman-like observer. Advantages and disadvantages of each method are discussed. On one hand, support vector machines training of transitory states would require a big amount of data in different situations, but the fault detection and isolation results are robust to variations in the input/operating point. On the other hand, the observer is model-based, and therefore does not require training, and it allows identification of the fault level, which is interesting for fault reconfiguration. But the observability of the system is ensured under specific conditions, related to the dynamics of the inputs and outputs. The whole fault detection and isolation scheme is evaluated using a wind t展开更多
Engine spark ignition is an important source for diagnosis of engine faults.Based on the waveform of the ignition pattern,a mechanic can guess what may be the potential malfunctioning parts of an engine with his/her e...Engine spark ignition is an important source for diagnosis of engine faults.Based on the waveform of the ignition pattern,a mechanic can guess what may be the potential malfunctioning parts of an engine with his/her experience and handbooks.However,this manual diagnostic method is imprecise because many spark ignition patterns are very similar.Therefore,a diagnosis needs many trials to identify the malfunctioning parts.Meanwhile the mechanic needs to disassemble and assemble the engine parts for verification.To tackle this problem,an intelligent diagnosis system was established based on ignition patterns.First,the captured patterns were normalized and compressed.Then wavelet packet transform（WPT） was employed to extract the representative features of the ignition patterns.Finally,a classification system was constructed by using multi-class support vector machines（SVM） and the extracted features.The classification system can intelligently classify the most likely engine fault so as to reduce the number of diagnosis trials.Experimental results show that SVM produces higher diagnosis accuracy than the traditional multilayer feedforward neural network.This is the first trial on the combination of WPT and SVM to analyze ignition patterns and diagnose automotive engines.展开更多
Support Vector Machines （SVM） is a new general machine-learning tool based on structural risk minimization principle. This characteristic is very signific ant for the fault diagnostics when the number of fault sampl...Support Vector Machines （SVM） is a new general machine-learning tool based on structural risk minimization principle. This characteristic is very signific ant for the fault diagnostics when the number of fault samples is limited. Considering that SVM theory is originally designed for a two-class classification, a hybrid SVM scheme is proposed for multi-fault classification of rotating machinery in our paper. Two SVM strategies, 1-v-1 （one versus one） and 1-v-r （one versus rest）, are respectively adopted at different classification levels. At the parallel classification level, using l-v-1 strategy, the fault features extracted by various signal analysis methods are transferred into the multiple parallel SVM and the local classification results are obtained. At the serial classification level, these local results values are fused by one serial SVM based on 1-v-r strategy. The hybrid SVM scheme introduced in our paper not only generalizes the performance of signal binary SVMs but improves the precision and reliability of the fault classification results. The actually testing results show the availability suitability of this new method.展开更多
The batch splitting scheduling problem has recently become a major target in manufacturing systems, and the researchers have obtained great achievements, whereas most of existing related researches focus on equal-size...The batch splitting scheduling problem has recently become a major target in manufacturing systems, and the researchers have obtained great achievements, whereas most of existing related researches focus on equal-sized and consistent-sized batch splitting scheduling problem, and solve the problem by fixing the number of sub-batches, or the sub-batch sizes, or both. Under such circumstance and to provide a practical method for production scheduling in batch production mode, a study was made on the batch splitting scheduling problem on alternative machines, based on the objective to minimize the makespan. A scheduling approach was presented to address the variable-sized batch splitting scheduling problem in job shops trying to optimize both the number of sub-bathes and the sub-batch sizes, based on differential evolution（DE）, making full use of the finding that the sum of values of genes in one chromosome remains the same before and after mutation in DE. Considering before-arrival set-up time and processing time separately, a variable-sized batch splitting scheduling model was established and a new hybrid algorithm was brought forward to solve both the batch splitting problem and the batch scheduling problem. A new parallel chromosome representation was adopted, and the batch scheduling chromosome and the batch splitting chromosome were treated separately during the global search procedure, based on self-adaptive DE and genetic crossover operator, respectively. A new local search method was further designed to gain a better performance. A solution consists of the optimum number of sub-bathes for each operation per job, the optimum batch size for each sub-batch and the optimum sequence of sub-batches. Computational experiments of four test instances and a realistic problem in a speaker workshop were performed to testify the effectiveness of the proposed scheduling method. The study takes advantage of DE＇s distinctive feature, and employs the algorithm as a solution approach, and thereby deepens and enriches th展开更多
基金Supported by the Key Program of the National Natural Science Foundation of China (Grant Nos.50635030 and 59835200)the Chinese National Programs for Science and Technology Development (Grant No.2005BA429C)the Key Proliferation Project for Scientific and Technological Fruits of China (Grant No.2005EC000119)
文摘The theoretical studies of bionics of machinery have great scientific significance, and the development of bionic machines has large practical values in the field of engineering and technology. Through the rigorous selection process of evolution, the survived living organisms have successfully developed outstanding abilities to adapt to their surroundings and to reproduce their offspring. In this review,we interpreted the fundamental principles of anti-adhesion and anti-resistance of soil animals by reviewing the current status in this research field and summarizing the work of the research group at Jilin University of China in the past decades. The principles and technologies used in morphology bionics,electric-osmosis bionics,flexibility bionics,configuration bionics and coupling bionics were examined.Finally,the applications of the engineering bionics and their extensive prospects were introduced.
文摘This study aims to develop several optimization techniques for predicting advance rate of tunnel boring machine(TBM)in different weathered zones of granite.For this purpose,extensive field and laboratory studies have been conducted along the 12,649 m of the Pahang-Selangor raw water transfer tunnel in Malaysia.Rock properties consisting of uniaxial compressive strength(UCS),Brazilian tensile strength(BTS),rock mass rating(RMR),rock quality designation(RQD),quartz content(q)and weathered zone as well as machine specifications including thrust force and revolution per minute(RPM)were measured to establish comprehensive datasets for optimization.Accordingly,to estimate the advance rate of TBM,two new hybrid optimization techniques,i.e.an artificial neural network(ANN)combined with both imperialist competitive algorithm(ICA)and particle swarm optimization(PSO),were developed for mechanical tunneling in granitic rocks.Further,the new hybrid optimization techniques were compared and the best one was chosen among them to be used for practice.To evaluate the accuracy of the proposed models for both testing and training datasets,various statistical indices including coefficient of determination(R^2),root mean square error(RMSE)and variance account for(VAF)were utilized herein.The values of R^2,RMSE,and VAF ranged in 0.939-0.961,0.022-0.036,and 93.899-96.145,respectively,with the PSO-ANN hybrid technique demonstrating the best performance.It is concluded that both the optimization techniques,i.e.PSO-ANN and ICA-ANN,could be utilized for predicting the advance rate of TBMs;however,the PSO-ANN technique is superior.
基金the National Nature Science Foundation of China (60775047, 60402024)
文摘The support vector machine （SVM） is a novel machine learning method, which has the ability to approximate nonlinear functions with arbitrary accuracy. Setting parameters well is very crucial for SVM learning results and generalization ability, and now there is no systematic, general method for parameter selection. In this article, the SVM parameter selection for function approximation is regarded as a compound optimization problem and a mutative scale chaos optimization algorithm is employed to search for optimal paraxneter values. The chaos optimization algorithm is an effective way for global optimal and the mutative scale chaos algorithm could improve the search efficiency and accuracy. Several simulation examples show the sensitivity of the SVM parameters and demonstrate the superiority of this proposed method for nonlinear function approximation.
基金Project 06KJD470182 supported by the Jiangsu Educational Natural Science Foundation of china
文摘A new method based on principal component analysis (PCA) and support vector machines (SVMs) is proposed for fault diagnosis of mine hoists. PCA is used to extract the principal features associated with the gearbox. Then, with the irrelevant gearbox variables removed, the remaining gearbox, the hydraulic system and the wire rope parameters were used as input to a multi-class SVM. The SVM is first trained by using the one class-based multi-class optimization algorithm and it is then applied to fault identification. Comparison of various methods showed the PCA-SVM method successfully removed redundancy to solve the dimensionality curse. These results show that the algorithm using the RBF kernel function for the SVM had the best classification properties.
基金supported by the National Natural Science Foundation of China (Grant Nos.50535010, 50675151)National High-Tech Research and Development Program of China (Grant No.2007AA042001)Ph.D. Programs Foundation of Ministry of Education of China (Grant No.20060056018)
文摘A study of dimensional synthesis of a 3-DOF parallel manipulator with coupling of translation and rotation is carried out.The architecture of the manipulator is composed of a moving platform attached to a fixed base through three identical PRS(prismatic-revolute-spherical) serial limbs,whose unique topology leads to the physical unit inconsistency of the conventional Jacobian matrix and the emergence of the parasitic motion.Then this paper introduces a kinetostatic performance index of the manipulator based on the condition of a dimensionally homogeneous Jacobian matrix,later,the workspace of the aforementioned manipulator is searched and the influence of the crucial design variables on the workspace is analyzed.Finally,a dimensional synthesis method of the manipulator is proposed,which may be regarded as a nonlinear programming problem with subject to the parasitic motion and other several engineering constraints.
基金Foundation item:Project (2006BAB02A02) supported by the National Key Technology R&D Program during the 11th Five-year Plan Period of ChinaProject (CX2011B119) supported by the Graduated Students' Research and Innovation Fund of Hunan Province, ChinaProject (2009ssxt230) supported by the Central South University Innovation Fund,China
文摘Aiming at the problems of the traditional method of assessing distribution of particle size in bench blasting, a support vector machines （SVMs） regression methodology was used to predict the mean particle size （X50） resulting from rock blast fragmentation in various mines based on the statistical learning theory. The data base consisted of blast design parameters, explosive parameters, modulus of elasticity and in-situ block size. The seven input independent variables used for the SVMs model for the prediction of X50 of rock blast fragmentation were the ratio of bench height to drilled burden （H/B）, ratio of spacing to burden （S/B）, ratio of burden to hole diameter （B/D）, ratio of stemming to burden （T/B）, powder factor （Pf）, modulus of elasticity （E） and in-situ block size （XB）. After using the 90 sets of the measured data in various mines and rock formations in the world for training and testing, the model was applied to 12 another blast data for validation of the trained support vector regression （SVR） model. The prediction results of SVR were compared with those of artificial neural network （ANN）, multivariate regression analysis （MVRA） models, conventional Kuznetsov method and the measured X50 values. The proposed method shows promising results and the prediction accuracy of SVMs model is acceptable.
基金supported by Tianjin Research Program of Application Foundation and Advanced Technology of China (Grant No.11JCZDJC22700)National Natural Science Foundation of China (GrantNo. 51075295,Grant No. 50675151)+1 种基金National High-tech Research and Development Program of China (863 Program,Grant No.2007AA042001)PhD Programs Foundation of Ministry of Education of China (Grant No. 20060056018)
文摘Parallel kinematic machines （PKMs） have the advantages of a compact structure,high stiffness,a low moving inertia,and a high load/weight ratio.PKMs have been intensively studied since the 1980s,and are still attracting much attention.Compared with extensive researches focus on their type/dimensional synthesis,kinematic/dynamic analyses,the error modeling and separation issues in PKMs are not studied adequately,which is one of the most important obstacles in its commercial applications widely.Taking a 3-PRS parallel manipulator as an example,this paper presents a separation method of source errors for 3-DOF parallel manipulator into the compensable and non-compensable errors effectively.The kinematic analysis of 3-PRS parallel manipulator leads to its six-dimension Jacobian matrix,which can be mapped into the Jacobian matrix of actuations and constraints,and then the compensable and non-compensable errors can be separated accordingly.The compensable errors can be compensated by the kinematic calibration,while the non-compensable errors may be adjusted by the manufacturing and assembling process.Followed by the influence of the latter,i.e.,the non-compensable errors,on the pose error of the moving platform through the sensitivity analysis with the aid of the Monte-Carlo method,meanwhile,the configurations of the manipulator are sought as the pose errors of the moving platform approaching their maximum.The compensable and non-compensable errors in limited-DOF parallel manipulators can be separated effectively by means of the Jacobian matrix of actuations and constraints,providing designers with an informative guideline to taking proper measures for enhancing the pose accuracy via component tolerancing and/or kinematic calibration,which can lay the foundation for the error distinguishment and compensation.
基金Project (50934006) supported by the National Natural Science Foundation of ChinaProject (2010CB732004) supported by the National Basic Research Program of ChinaProject (CX2011B119) supported by the Graduated Students’ Research and Innovation Fund Project of Hunan Province of China
文摘The purpose of this study is to apply some statistical and soft computing methods such as Fisher discriminant analysis （FDA） and support vector machines （SVMs） methodology to the determination of pillar stability for underground mines selected from various coal and stone mines by using some index and mechanical properties, including the width, the height, the ratio of the pillar width to its height, the uniaxial compressive strength of the rock and pillar stress. The study includes four main stages： sampling, testing, modeling and assessment of the model performances. During the modeling stage, two pillar stability prediction models were investigated with FDA and SVMs methodology based on the statistical learning theory. After using 40 sets of measured data in various mines in the world for training and testing, the model was applied to other 6 data for validating the trained proposed models. The prediction results of SVMs were compared with those of FDA as well as the measured field values. The general performance of models developed in this study is close; however, the SVMs exhibit the best performance considering the performance index with the correct classification rate Prs by re-substitution method and Pcv by cross validation method. The results show that the SVMs approach has the potential to be a reliable and practical tool for determination of pillar stability for underground mines.
文摘With the applications of high technology,a catastrophic failure of CNC equipment rarely occurs at normal operation conditions.So it is difficult for traditional reliability assessment methods based on time-to-failure distributions to deduce the reliability level.This paper presents a novel reliability assessment methodology to estimate the reliability level of equipment with machining performance degradation data when only a few samples are available.The least squares support vector machines(LS-SVM) are introduced to analyze the performance degradation process on the equipment.A two-stage parameter optimization and searching method is proposed to improve the LS-SVM regression performance and a reliability assessment model based on the LS-SVM is built.A machining performance degradation experiment has been carried out on an OTM650 machine tool to validate the effectiveness of the proposed reliability assessment methodology.
基金Acknowledgments This work was supported by National Natural Science Foundation of China （Grant no. 60971089）, National Electronic Development Foundation of China （Grant no. 2009537）, Jilin Province Science and Tech- nology Department Project of China （Grant no. 20090502）.
文摘Particle Swarm Optimization （PSO） is a popular and bionic algorithm based on the social behavior associated with bird flocking for optimization problems. To maintain the diversity of swarms, a few studies of multi-swarm strategy have been reported. However, the competition among swarms, reservation or destruction of a swarm, has not been considered further. In this paper, we formulate four rules by introducing the mechanism for survival of the fittest, which simulates the competition among the swarms. Based on the mechanism, we design a modified Multi-Swarm PSO （MSPSO） to solve discrete problems, which consists of a number of sub-swarms and a multi-swarm scheduler that can monitor and control each sub-swarm using the rules. To further settle the feature selection problems, we propose an Improved Feature Selection （1FS） method by integrating MSPSO, Support Vector Machines （SVM） with F-score method. The IFS method aims to achieve higher generalization capa- bility through performing kernel parameter optimization and feature selection simultaneously. The performance of the proposed method is compared with that of the standard PSO based, Genetic Algorithm （GA） based and the grid search based mcthods on 10 benchmark datasets, taken from UCI machine learning and StatLog databases. The numerical results and statistical analysis show that the proposed IFS method performs significantly better than the other three methods in terms of prediction accuracy with smaller subset of features.
文摘The robust parameter design method is a traditional approach to robust experimental design that seeks to obtain the optimal combination of factors/levels. To overcome some of the defects of the inflatable wing parameter design method, this paper proposes an optimization design scheme based on orthogonal testing and support vector machines （SVMs）. Orthogonal testing design is used to estimate the appropriate initial value and variation domain of each variable to decrease the number of iterations and improve the identification accuracy and efficiency. Orthogonal tests consisting of three factors and three levels are designed to analyze the parameters of pressure, uniform applied load and the number of chambers that affect the bending response of inflatable wings. An SVM intelligent model is established and limited orthogonal test swatches are studied. Thus, the precise relationships between each parameter and product quality features, as well the signal-to-noise ratio （SNR）, can be obtained. This can guide general technological design optimization.
基金Supported by the National Natural Science Foundation of China (No.60574038) and the Open Project Program of the State Key Laboratory of Bioreactor Engineering/ECUST.
文摘In this article, a nonlinear dynamic multiway partial least squares (MPLS) based on support vector machines (SVM) is developed for on-line fault detection in batch processes. The approach, referred to as SVM-based DMPLS, integrates the SVM with the MPLS model. Process data from normal historical batches are used to develop the MPLS model, and a series of single-input-single-output SVM networks are adopted to approximate nonlinear inner relationship between input and output variables. In addition, the application of a time-lagged window technique not only makes the complementarities of unmeasured data of the monitored batch unnecessary, but also significantly reduces the computation and storage requirements in comparison with the traditional MPLS. The proposed approach is validated by a simulation study of on-line fault detection for a fed-batch penicillin production.
基金supported by the National Natural Science Foundation of China(6113200291338101+3 种基金91338108)the National S&T Major Project(2011ZX03004-001-01)the Research Fund of Tsinghua University(2011Z05117)the Co-innovation Laboratory of Aerospace Broadband Network Technology
文摘Due to the limited transmission resources for data relay in the tracking and data relay satellite system （TDRSS）, there are many job requirements in busy days which will be discarded in the conventional job scheduling model. Therefore, the improvement of scheduling efficiency in the TDRSS can not only help to increase the resource utilities, but also to reduce the scheduling failure ratio. A model of nonhomogeneous parallel machines scheduling problems with time window （NPM-TW） is firstly built up for the TDRSS, considering the distinct features of the variable preparation time and the nonhomogeneous transmission rates for different types of antennas on each tracking and data relay satellite （TDRS）. Then, an adaptive subsequence adjustment （ASA） framework with evolutionary asymmetric path-relinking （EvAPR） is proposed to solve this problem, in which an asymmetric progressive crossover operation is involved to overcome the local optima by the conventional job inserting methods. The numerical results show that, compared with the classical greedy randomized adaptive search procedure （GRASP） algorithm, the scheduling failure ratio of jobs can be reduced over 11% on average by the proposed ASA with EvAPR.
基金National Key Basic Research Program of China,No.2010CB428403National Grand Science and Technology Special Project of Water Pollution Control and Improvement,No.2009ZX07210-006
文摘Sensitivity analysis of hydrological model is the key for model uncertainty quantification. However, how to effectively validate model and identify the dominant parameters for distributed hydrological models is a bottle-neck to achieve parameters optimization. For this reason, a new approach was proposed in this paper, in which the support vector machine was used to construct the response surface at first. Then it integrates the SVM-based response surface with the Sobol＇ method, i.e. the RSMSoboI＇ method, to quantify the parameter sensi- tivities. In this work, the distributed time-variant gain model （DTVGM） was applied to the Huaihe River Basin, which was used as a case to verify its validity and feasibility. We selected three objective functions （i.e. water balance coefficient WB, Nash-Sutcliffe efficiency coefficient NS, and correlation coefficient RC） to assess the model performance as the output responses for sensitivity analysis. The results show that the parameters gl and g2 are most important for all the objective functions, and they are almost the same to that of the classical approach. Furthermore, the RSMSobol method can not only achieve the quantification of the sensitivity, and also reduce the computational cost, with good accuracy compared to the classical approach. And this approach will be effective and reliable in the global sensitivity analysis for a complex modelling system.
基金supported by the National Natural Science Foundation of China(Grant Nos. 40571115 and 40271078)the National Hi-Tech Research and Development Program of China(Grant No. 2006AA10Z203)
文摘Hyperspectral reflectance (350-2500 nm) measurements were made over two experimental rice fields containing two cultivars treated with three levels of nitrogen application.Four different transformations of the reflectance data were analyzed for their capability to predict rice biophysical parameters,comprising leaf area index (LAI;m-2 green leaf area m-2 soil) and green leaf chlorophyll density (GLCD;mg chlorophyll m 2 soil),using stepwise multiple regression (SMR) models and support vector machines (SVMs).Four transformations of the rice canopy data were made,comprising reflectances (R),first-order derivative reflectances (D1),second-order derivative reflectances (D2),and logarithm transformation of reflectances (LOG).The polynomial kernel (POLY) of the SVM using R was the best model to predict rice LAI,with a root mean square error (RMSE) of 1.0496 LAI units.The analysis of variance kernel of SVM using LOG was the best model to predict rice GLCD,with an RMSE of 523.0741 mg m-2.The SVM approach was not only superior to SMR models for predicting the rice biophysical parameters,but also provided a useful exploratory and predictive tool for analyzing different transformations of reflectance data.
文摘Support vector machines and a Kalman-like observer are used for fault detection and isolation in a variable speed horizontalaxis wind turbine composed of three blades and a full converter. The support vector approach is data-based and is therefore robust to process knowledge. It is based on structural risk minimization which enhances generalization even with small training data set and it allows for process nonlinearity by using flexible kernels. In this work, a radial basis function is used as the kernel. Different parts of the process are investigated including actuators and sensors faults. With duplicated sensors, sensor faults in blade pitch positions,generator and rotor speeds can be detected. Faults of type stuck measurements can be detected in 2 sampling periods. The detection time of offset/scaled measurements depends on the severity of the fault and on the process dynamics when the fault occurs. The converter torque actuator fault can be detected within 2 sampling periods. Faults in the actuators of the pitch systems represents a higher difficulty for fault detection which is due to the fact that such faults only affect the transitory state(which is very fast) but not the final stationary state. Therefore, two methods are considered and compared for fault detection and isolation of this fault: support vector machines and a Kalman-like observer. Advantages and disadvantages of each method are discussed. On one hand, support vector machines training of transitory states would require a big amount of data in different situations, but the fault detection and isolation results are robust to variations in the input/operating point. On the other hand, the observer is model-based, and therefore does not require training, and it allows identification of the fault level, which is interesting for fault reconfiguration. But the observability of the system is ensured under specific conditions, related to the dynamics of the inputs and outputs. The whole fault detection and isolation scheme is evaluated using a wind t
基金supported by University of Macao Research Grant,China (Grant No. RG057/08-09S/VCM/FST, Grant No. UL011/09-Y1/ EME/ WPK01/FST)
文摘Engine spark ignition is an important source for diagnosis of engine faults.Based on the waveform of the ignition pattern,a mechanic can guess what may be the potential malfunctioning parts of an engine with his/her experience and handbooks.However,this manual diagnostic method is imprecise because many spark ignition patterns are very similar.Therefore,a diagnosis needs many trials to identify the malfunctioning parts.Meanwhile the mechanic needs to disassemble and assemble the engine parts for verification.To tackle this problem,an intelligent diagnosis system was established based on ignition patterns.First,the captured patterns were normalized and compressed.Then wavelet packet transform（WPT） was employed to extract the representative features of the ignition patterns.Finally,a classification system was constructed by using multi-class support vector machines（SVM） and the extracted features.The classification system can intelligently classify the most likely engine fault so as to reduce the number of diagnosis trials.Experimental results show that SVM produces higher diagnosis accuracy than the traditional multilayer feedforward neural network.This is the first trial on the combination of WPT and SVM to analyze ignition patterns and diagnose automotive engines.
文摘Support Vector Machines （SVM） is a new general machine-learning tool based on structural risk minimization principle. This characteristic is very signific ant for the fault diagnostics when the number of fault samples is limited. Considering that SVM theory is originally designed for a two-class classification, a hybrid SVM scheme is proposed for multi-fault classification of rotating machinery in our paper. Two SVM strategies, 1-v-1 （one versus one） and 1-v-r （one versus rest）, are respectively adopted at different classification levels. At the parallel classification level, using l-v-1 strategy, the fault features extracted by various signal analysis methods are transferred into the multiple parallel SVM and the local classification results are obtained. At the serial classification level, these local results values are fused by one serial SVM based on 1-v-r strategy. The hybrid SVM scheme introduced in our paper not only generalizes the performance of signal binary SVMs but improves the precision and reliability of the fault classification results. The actually testing results show the availability suitability of this new method.
基金supported by National Hi-tech Research and Development Program of China (863 Program, Grant No. 2007AA04Z155)National Natural Science Foundation of China (Grant No. 60970021)Zhejiang Provincial Natural Science Foundation of China (Grant No. Y1090592)
文摘The batch splitting scheduling problem has recently become a major target in manufacturing systems, and the researchers have obtained great achievements, whereas most of existing related researches focus on equal-sized and consistent-sized batch splitting scheduling problem, and solve the problem by fixing the number of sub-batches, or the sub-batch sizes, or both. Under such circumstance and to provide a practical method for production scheduling in batch production mode, a study was made on the batch splitting scheduling problem on alternative machines, based on the objective to minimize the makespan. A scheduling approach was presented to address the variable-sized batch splitting scheduling problem in job shops trying to optimize both the number of sub-bathes and the sub-batch sizes, based on differential evolution（DE）, making full use of the finding that the sum of values of genes in one chromosome remains the same before and after mutation in DE. Considering before-arrival set-up time and processing time separately, a variable-sized batch splitting scheduling model was established and a new hybrid algorithm was brought forward to solve both the batch splitting problem and the batch scheduling problem. A new parallel chromosome representation was adopted, and the batch scheduling chromosome and the batch splitting chromosome were treated separately during the global search procedure, based on self-adaptive DE and genetic crossover operator, respectively. A new local search method was further designed to gain a better performance. A solution consists of the optimum number of sub-bathes for each operation per job, the optimum batch size for each sub-batch and the optimum sequence of sub-batches. Computational experiments of four test instances and a realistic problem in a speaker workshop were performed to testify the effectiveness of the proposed scheduling method. The study takes advantage of DE＇s distinctive feature, and employs the algorithm as a solution approach, and thereby deepens and enriches th
