There are multiple operating modes in the real industrial process, and the collected data follow the complex multimodal distribution, so most traditional process monitoring methods are no longer applicable because the...There are multiple operating modes in the real industrial process, and the collected data follow the complex multimodal distribution, so most traditional process monitoring methods are no longer applicable because their presumptions are that sampled-data should obey the single Gaussian distribution or non-Gaussian distribution. In order to solve these problems, a novel weighted local standardization(WLS) strategy is proposed to standardize the multimodal data, which can eliminate the multi-mode characteristics of the collected data, and normalize them into unimodal data distribution. After detailed analysis of the raised data preprocessing strategy, a new algorithm using WLS strategy with support vector data description(SVDD) is put forward to apply for multi-mode monitoring process. Unlike the strategy of building multiple local models, the developed method only contains a model without the prior knowledge of multi-mode process. To demonstrate the proposed method's validity, it is applied to a numerical example and a Tennessee Eastman(TE) process. Finally, the simulation results show that the WLS strategy is very effective to standardize multimodal data, and the WLS-SVDD monitoring method has great advantages over the traditional SVDD and PCA combined with a local standardization strategy(LNS-PCA) in multi-mode process monitoring.展开更多
In order to improve measurement accuracy of moving target signals, an automatic target recognition model of moving target signals was established based on empirical mode decomposition(EMD) and support vector machine(S...In order to improve measurement accuracy of moving target signals, an automatic target recognition model of moving target signals was established based on empirical mode decomposition(EMD) and support vector machine(SVM). Automatic target recognition process on the nonlinear and non-stationary of Doppler signals of military target by using automatic target recognition model can be expressed as follows. Firstly, the nonlinearity and non-stationary of Doppler signals were decomposed into a set of intrinsic mode functions(IMFs) using EMD. After the Hilbert transform of IMF, the energy ratio of each IMF to the total IMFs can be extracted as the features of military target. Then, the SVM was trained through using the energy ratio to classify the military targets, and genetic algorithm(GA) was used to optimize SVM parameters in the solution space. The experimental results show that this algorithm can achieve the recognition accuracies of 86.15%, 87.93%, and 82.28% for tank, vehicle and soldier, respectively.展开更多
The elastic support/dry friction damper is a type of damper which is used for active vibration control in a rotor system.To establish the analytical model of this type of damper,a two-dimensional friction model-ball/p...The elastic support/dry friction damper is a type of damper which is used for active vibration control in a rotor system.To establish the analytical model of this type of damper,a two-dimensional friction model-ball/plate model was proposed.By using this ball/plate model,a dynamics model of rotor with elastic support/dry friction dampers was established and experimentally verified.Moreover,the damping performance of the elastic support/dry friction damper was studied numerically with respect to some variable parameters.The numerical study shows that the damping performance of the elastic support/dry friction damper is closely related to the stiffness distribution of the rotor-support system,the damper location,the pressing force between the moving and stationary disk,the friction coefficient,the tangential contact stiffness of the contact interface,and the stiffness of the stationary disk.In general,the damper should be located on an elastic support which has a large vibration amplitude in order to achieve a better damping performance,and the more vibration energy in this elastic support concentrates,the better performance of the damper will be.The larger the tangential contact stiffness of the contact interface,and the stiffness of the stationary disk are,the better performance of the damper will be.There will be an optimal value of the friction force at which the damper performs best.展开更多
Many animals possess actively movable tactile sensors in their heads,to explore the near-range space.During locomotion,an antenna is used in near range orientation,for example,in detecting,localizing,probing,and negot...Many animals possess actively movable tactile sensors in their heads,to explore the near-range space.During locomotion,an antenna is used in near range orientation,for example,in detecting,localizing,probing,and negotiating obstacles.A bionic tactile sensor used in the present work was inspired by the antenna of the stick insects.The sensor is able to detect an obstacle and its location in 3 D(Three dimensional) space.The vibration signals are analyzed in the frequency domain using Fast Fourier Transform(FFT) to estimate the distances.Signal processing algorithms,Artificial Neural Network(ANN) and Support Vector Machine(SVM) are used for the analysis and prediction processes.These three prediction techniques are compared for both distance estimation and material classification processes.When estimating the distances,the accuracy of estimation is deteriorated towards the tip of the probe due to the change in the vibration modes.Since the vibration data within that region have high a variance,the accuracy in distance estimation and material classification are lower towards the tip.The change in vibration mode is mathematically analyzed and a solution is proposed to estimate the distance along the full range of the probe.展开更多
A new method for estimating significant wave height（SWH） from advanced synthetic aperture radar（ASAR） wave mode data based on a support vector machine（SVM） regression model is presented. The model is established...A new method for estimating significant wave height（SWH） from advanced synthetic aperture radar（ASAR） wave mode data based on a support vector machine（SVM） regression model is presented. The model is established based on a nonlinear relationship between σ0, the variance of the normalized SAR image, SAR image spectrum spectral decomposition parameters and ocean wave SWH. The feature parameters of the SAR images are the input parameters of the SVM regression model, and the SWH provided by the European Centre for Medium-range Weather Forecasts（ECMWF） is the output parameter. On the basis of ASAR matching data set, a particle swarm optimization（PSO） algorithm is used to optimize the input kernel parameters of the SVM regression model and to establish the SVM model. The SWH estimation results yielded by this model are compared with the ECMWF reanalysis data and the buoy data. The RMSE values of the SWH are 0.34 and 0.48 m, and the correlation coefficient is 0.94 and 0.81, respectively. The results show that the SVM regression model is an effective method for estimating the SWH from the SAR data. The advantage of this model is that SAR data may serve as an independent data source for retrieving the SWH, which can avoid the complicated solution process associated with wave spectra.展开更多
Eight casing failure modes and 32 risk factors in oil and gas wells are given in this paper. According to the quantitative analysis of the influence degree and occurrence probability of risk factors, the Borda counts ...Eight casing failure modes and 32 risk factors in oil and gas wells are given in this paper. According to the quantitative analysis of the influence degree and occurrence probability of risk factors, the Borda counts for failure modes are obtained with the Borda method. The risk indexes of failure modes are derived from the Borda matrix. Based on the support vector machine （SVM）, a casing life prediction model is established. In the prediction model, eight risk indexes are defined as input vectors and casing life is defined as the output vector. The ideal model parameters are determined with the training set from 19 wells with casing failure. The casing life prediction software is developed with the SVM model as a predictor. The residual life of 60 wells with casing failure is predicted with the software, and then compared with the actual casing life. The comparison results show that the casing life prediction software with the SVM model has high accuracy.展开更多
There are many state-level poverty-stricken counties in the dry-hot valley areas of Jinsha River in China,with a wide range of poverty and extreme degree of poverty.The industry-supporting poverty alleviation ranks fi...There are many state-level poverty-stricken counties in the dry-hot valley areas of Jinsha River in China,with a wide range of poverty and extreme degree of poverty.The industry-supporting poverty alleviation ranks first in the"five batches"of China's targeted poverty alleviation strategy.The practice of planting in Luquan County since 2017 shows that the valleys and slopes on the dry-hot valley areas of Jinsha River with an altitude above 1800 m have wide land suitable for sorghum planting,and suitable for introduction and planting.In recent years,the county has adopted the mode of enterprise+government+cooperative+poor household,introduced the Langzhitang wine factory,developed sorghum planting with large-scale,industrialized and specialized features to achieve stable income growth for the poor,and significant results have been achieved.Based on many field surveys,household surveys,and interviews with county and village leaders,this paper analyzes the specific practices and main effects of the county's poverty alleviation model by developing sorghum planting industry,aiming to provide necessary reference for the targeted poverty alleviation and poverty alleviation in similar areas of Yunnan Province and other provinces.展开更多
本文通过将故障模式与产品树融合,构建动态维修物料清单(MBOM:Maintenance Bill of Material)的方法,旨在解决传统MBOM存在的局限性。传统MBOM的静态属性难以有效适应故障模式动态变化的特点,导致备件、物料等管理效率低下和维修决策支...本文通过将故障模式与产品树融合,构建动态维修物料清单(MBOM:Maintenance Bill of Material)的方法,旨在解决传统MBOM存在的局限性。传统MBOM的静态属性难以有效适应故障模式动态变化的特点,导致备件、物料等管理效率低下和维修决策支持不足。本文通过提出一种基于故障模式的动态MBOM构建方法,以结构化的故障模式知识库为核心,设计了故障模式与维修任务的关联机制,以及数据驱动的动态MBOM生成与更新策略。通过案例研究表明所提出的动态MBOM构建方法能够有效提升备件、物料等管理的精确性和动态适应性,优化维修作业流程。该研究的创新之处在于,通过构建的基于故障模式的动态MBOM的方法体系,为提升航空电子维修保障能力和降低维护成本提供了新的思路和方法。展开更多
基金Project(61374140)supported by the National Natural Science Foundation of China
文摘There are multiple operating modes in the real industrial process, and the collected data follow the complex multimodal distribution, so most traditional process monitoring methods are no longer applicable because their presumptions are that sampled-data should obey the single Gaussian distribution or non-Gaussian distribution. In order to solve these problems, a novel weighted local standardization(WLS) strategy is proposed to standardize the multimodal data, which can eliminate the multi-mode characteristics of the collected data, and normalize them into unimodal data distribution. After detailed analysis of the raised data preprocessing strategy, a new algorithm using WLS strategy with support vector data description(SVDD) is put forward to apply for multi-mode monitoring process. Unlike the strategy of building multiple local models, the developed method only contains a model without the prior knowledge of multi-mode process. To demonstrate the proposed method's validity, it is applied to a numerical example and a Tennessee Eastman(TE) process. Finally, the simulation results show that the WLS strategy is very effective to standardize multimodal data, and the WLS-SVDD monitoring method has great advantages over the traditional SVDD and PCA combined with a local standardization strategy(LNS-PCA) in multi-mode process monitoring.
基金Projects(61471370,61401479)supported by the National Natural Science Foundation of China
文摘In order to improve measurement accuracy of moving target signals, an automatic target recognition model of moving target signals was established based on empirical mode decomposition(EMD) and support vector machine(SVM). Automatic target recognition process on the nonlinear and non-stationary of Doppler signals of military target by using automatic target recognition model can be expressed as follows. Firstly, the nonlinearity and non-stationary of Doppler signals were decomposed into a set of intrinsic mode functions(IMFs) using EMD. After the Hilbert transform of IMF, the energy ratio of each IMF to the total IMFs can be extracted as the features of military target. Then, the SVM was trained through using the energy ratio to classify the military targets, and genetic algorithm(GA) was used to optimize SVM parameters in the solution space. The experimental results show that this algorithm can achieve the recognition accuracies of 86.15%, 87.93%, and 82.28% for tank, vehicle and soldier, respectively.
基金supported by the National Natural Science Foundation of China(No.51405393)
文摘The elastic support/dry friction damper is a type of damper which is used for active vibration control in a rotor system.To establish the analytical model of this type of damper,a two-dimensional friction model-ball/plate model was proposed.By using this ball/plate model,a dynamics model of rotor with elastic support/dry friction dampers was established and experimentally verified.Moreover,the damping performance of the elastic support/dry friction damper was studied numerically with respect to some variable parameters.The numerical study shows that the damping performance of the elastic support/dry friction damper is closely related to the stiffness distribution of the rotor-support system,the damper location,the pressing force between the moving and stationary disk,the friction coefficient,the tangential contact stiffness of the contact interface,and the stiffness of the stationary disk.In general,the damper should be located on an elastic support which has a large vibration amplitude in order to achieve a better damping performance,and the more vibration energy in this elastic support concentrates,the better performance of the damper will be.The larger the tangential contact stiffness of the contact interface,and the stiffness of the stationary disk are,the better performance of the damper will be.There will be an optimal value of the friction force at which the damper performs best.
文摘Many animals possess actively movable tactile sensors in their heads,to explore the near-range space.During locomotion,an antenna is used in near range orientation,for example,in detecting,localizing,probing,and negotiating obstacles.A bionic tactile sensor used in the present work was inspired by the antenna of the stick insects.The sensor is able to detect an obstacle and its location in 3 D(Three dimensional) space.The vibration signals are analyzed in the frequency domain using Fast Fourier Transform(FFT) to estimate the distances.Signal processing algorithms,Artificial Neural Network(ANN) and Support Vector Machine(SVM) are used for the analysis and prediction processes.These three prediction techniques are compared for both distance estimation and material classification processes.When estimating the distances,the accuracy of estimation is deteriorated towards the tip of the probe due to the change in the vibration modes.Since the vibration data within that region have high a variance,the accuracy in distance estimation and material classification are lower towards the tip.The change in vibration mode is mathematically analyzed and a solution is proposed to estimate the distance along the full range of the probe.
基金The National Key Research and Development Program of China under contract Nos 2016YFA0600102 and2016YFC1401007the National Natural Science Youth Foundation of China under contract No.61501130the Natural Science Foundation of China under contract No.41406207
文摘A new method for estimating significant wave height（SWH） from advanced synthetic aperture radar（ASAR） wave mode data based on a support vector machine（SVM） regression model is presented. The model is established based on a nonlinear relationship between σ0, the variance of the normalized SAR image, SAR image spectrum spectral decomposition parameters and ocean wave SWH. The feature parameters of the SAR images are the input parameters of the SVM regression model, and the SWH provided by the European Centre for Medium-range Weather Forecasts（ECMWF） is the output parameter. On the basis of ASAR matching data set, a particle swarm optimization（PSO） algorithm is used to optimize the input kernel parameters of the SVM regression model and to establish the SVM model. The SWH estimation results yielded by this model are compared with the ECMWF reanalysis data and the buoy data. The RMSE values of the SWH are 0.34 and 0.48 m, and the correlation coefficient is 0.94 and 0.81, respectively. The results show that the SVM regression model is an effective method for estimating the SWH from the SAR data. The advantage of this model is that SAR data may serve as an independent data source for retrieving the SWH, which can avoid the complicated solution process associated with wave spectra.
基金support from "973 Project" (Contract No. 2010CB226706)
文摘Eight casing failure modes and 32 risk factors in oil and gas wells are given in this paper. According to the quantitative analysis of the influence degree and occurrence probability of risk factors, the Borda counts for failure modes are obtained with the Borda method. The risk indexes of failure modes are derived from the Borda matrix. Based on the support vector machine （SVM）, a casing life prediction model is established. In the prediction model, eight risk indexes are defined as input vectors and casing life is defined as the output vector. The ideal model parameters are determined with the training set from 19 wells with casing failure. The casing life prediction software is developed with the SVM model as a predictor. The residual life of 60 wells with casing failure is predicted with the software, and then compared with the actual casing life. The comparison results show that the casing life prediction software with the SVM model has high accuracy.
基金Supported by Commissioned Project of Office of Rural Work Leading Group of Kunming Municipal Committee of the Communist Party of China "Study on the Poverty Alleviation Model of Kunming City in the Context of World Poverty Reduction"
文摘There are many state-level poverty-stricken counties in the dry-hot valley areas of Jinsha River in China,with a wide range of poverty and extreme degree of poverty.The industry-supporting poverty alleviation ranks first in the"five batches"of China's targeted poverty alleviation strategy.The practice of planting in Luquan County since 2017 shows that the valleys and slopes on the dry-hot valley areas of Jinsha River with an altitude above 1800 m have wide land suitable for sorghum planting,and suitable for introduction and planting.In recent years,the county has adopted the mode of enterprise+government+cooperative+poor household,introduced the Langzhitang wine factory,developed sorghum planting with large-scale,industrialized and specialized features to achieve stable income growth for the poor,and significant results have been achieved.Based on many field surveys,household surveys,and interviews with county and village leaders,this paper analyzes the specific practices and main effects of the county's poverty alleviation model by developing sorghum planting industry,aiming to provide necessary reference for the targeted poverty alleviation and poverty alleviation in similar areas of Yunnan Province and other provinces.
文摘本文通过将故障模式与产品树融合,构建动态维修物料清单(MBOM:Maintenance Bill of Material)的方法,旨在解决传统MBOM存在的局限性。传统MBOM的静态属性难以有效适应故障模式动态变化的特点,导致备件、物料等管理效率低下和维修决策支持不足。本文通过提出一种基于故障模式的动态MBOM构建方法,以结构化的故障模式知识库为核心,设计了故障模式与维修任务的关联机制,以及数据驱动的动态MBOM生成与更新策略。通过案例研究表明所提出的动态MBOM构建方法能够有效提升备件、物料等管理的精确性和动态适应性,优化维修作业流程。该研究的创新之处在于,通过构建的基于故障模式的动态MBOM的方法体系,为提升航空电子维修保障能力和降低维护成本提供了新的思路和方法。
