The Balise Transmission Module(BTM)unit of the on-board train control system is a crucial component.Due to its unique installation position and complex environment,this unit has a higher fault rate within the on-board...The Balise Transmission Module(BTM)unit of the on-board train control system is a crucial component.Due to its unique installation position and complex environment,this unit has a higher fault rate within the on-board train control system.To conduct fault prediction for the BTM unit based on actual fault data,this study proposes a prediction method combining reliability statistics and machine learning,and achieves the fusion of prediction results from different dimensions through multi-method interactive validation.Firstly,a method for predicting equipment fault time targeting batch equipment is introduced.This method utilizes reliability statistics to construct a model of the remaining faultless operating time distribution considering uncertainty,thereby predicting the remaining faultless operating probability of the BTM unit.Secondly,considering the complexity of the BTM unit’s fault mechanism,the small sample size of fault cases,and the potential presence of multiple fault features in fault text records,an individual-oriented fault prediction method based on Bayesian-optimized Gradient Boosting Regression Tree(Bayes-GBRT)is proposed.This method achieves better prediction results compared to linear regression algorithms and random forest regression algorithms,with an average absolute error of only 0.224 years for predicting the fault time of this type of equipment.Finally,a multi-method interactive validation approach is proposed,enabling the fusion and validation of multi-dimensional results.The results indicate that the predicted fault time and the actual fault time conform to a log-normal distribution,and the parameter estimation results are basically consistent,verifying the accuracy and effectiveness of the prediction results.The above research findings can provide technical support for the maintenance and modification of BTM units,effectively reducing maintenance costs and ensuring the safe operation of high-speed railway,thus having practical engineering value for preventive maintenance.展开更多
In the process of designing self-elevating drilling unit, it is important, yet complicated, to use comparison and filtering to select the optimum scheme from the feasible ones. In this research, an index system and me...In the process of designing self-elevating drilling unit, it is important, yet complicated, to use comparison and filtering to select the optimum scheme from the feasible ones. In this research, an index system and methodology for the evaluation of self-elevating drilling unit was proposed. Based on this, a multi-objective combinatorial optimization model was developed, using the improved grey relation Analysis （GRA）, in which the analytic hierarchy process （AHP） was used to determine the weights of the evaluating indices. It considered the connections within the indices, reflecting the objective nature of things, and also considered the subjective interests of ship owners and the needs of designers. The evaluation index system and evaluation method can be used in the selection of an optimal scheme and advanced assessment. A case study shows the index system and evaluation method are scientific, reasonable, and easy to put into practice. At the same time, such an evaluation index system and evaluation method will be helpful for making decisions for other mobile platforms.展开更多
Quality function deployment （QFD） is a quality system, that can help to design novel products that meet customers＇ needs. Theory of inventive problem solving （TRIZ） is a very powerful tool in helping to solve dif...Quality function deployment （QFD） is a quality system, that can help to design novel products that meet customers＇ needs. Theory of inventive problem solving （TRIZ） is a very powerful tool in helping to solve difficult technical problems encountered in the design process. Introducing QFD and TRIZ into the conceptual design of the pumping unit combines advantages of these two theories, therefore meeting different demands of different users. It can tell us “What should we do it” with QFD and “How should we do it” with TRIZ. The conceptual design method, which is based on QFD and TRIZ, is introduced andused to analyze and evaluate the conceptual design project of a pumping unit.展开更多
Fluid-structure interaction （FSI） problems in microchannels play a prominent role in many engineering applications. The present study is an effort toward the simulation of flow in microchannel considering FSI. The b...Fluid-structure interaction （FSI） problems in microchannels play a prominent role in many engineering applications. The present study is an effort toward the simulation of flow in microchannel considering FSI. The bottom boundary of the microchannel is simulated by size-dependent beam elements for the finite element method （FEM） based on a modified cou- ple stress theory. The lattice Boltzmann method （LBM） using the D2Q13 LB model is coupled to the FEM in order to solve the fluid part of the FSI problem. Because of the fact that the LBM generally needs only nearest neighbor information, the algorithm is an ideal candidate for parallel computing. The simulations are carried out on graphics processing units （GPUs） using computed unified device architecture （CUDA）. In the present study, the governing equations are non-dimensionalized and the set of dimensionless groups is exhibited to show their effects on micro-beam displacement. The numerical results show that the displacements of the micro-beam predicted by the size-dependent beam element are smaller than those by the classical beam element.展开更多
The flow stress of ferrite/pearlite steel under uni-axial tension was simulated with finite element method (FEM) by applying commercial software MARC/MENTAT. Flow stress curves of ferrite/pearlite steels were calculat...The flow stress of ferrite/pearlite steel under uni-axial tension was simulated with finite element method (FEM) by applying commercial software MARC/MENTAT. Flow stress curves of ferrite/pearlite steels were calculated based on unit cell model. The effects of volume fraction, distribution and the aspect ratio of pearlite on tensile properties have been investigated.展开更多
Aiming at that the successive test data set of the strapdown inertial measurement unit is always small,a Bayesian method is used to study its statistical characteristics.Its prior and posterior distributions are set u...Aiming at that the successive test data set of the strapdown inertial measurement unit is always small,a Bayesian method is used to study its statistical characteristics.Its prior and posterior distributions are set up by the method and the pretest,sample and population information.Some statistical inferences can be made based on the posterior distribution.It can reduce the statistical analysis error in the case of small sample set.展开更多
得益于日趋完善的状态监测系统,抽水蓄能电站记录了海量机组运行信息,为开展抽蓄机组劣化趋势评估提供了可靠的数据保障。为此,提出了一种基于Optuna-CatBoost和CRITIC(Criteria Importance though Intercrieria Correlation)评价法的...得益于日趋完善的状态监测系统,抽水蓄能电站记录了海量机组运行信息,为开展抽蓄机组劣化趋势评估提供了可靠的数据保障。为此,提出了一种基于Optuna-CatBoost和CRITIC(Criteria Importance though Intercrieria Correlation)评价法的水电机组劣化评估方法。首先利用最大互信息系数(Maximal Information Coefficient)筛选出机组关键工况系数;然后利用Optuna对CatBoost进行参数寻优,建立Optuna-CatBoost水电机组劣化趋势评估模型;最后基于CRITIC评价法对各通道劣化序列客观赋权,生成机组整机劣化序列。试验结果表明,所提模型的精度优于其他对比模型,能很好地反映机组整机劣化趋势。展开更多
基于超声波时差测量流量的方法具有非接触、易安装、不改变流体的运动状态等优点,被广泛应用于油田井下流体流速测量分析领域,能够实时测量流体流速,准确分析管道中流体流量的变化。针对传统的超声波流量计功耗高、电路复杂的缺点,根据...基于超声波时差测量流量的方法具有非接触、易安装、不改变流体的运动状态等优点,被广泛应用于油田井下流体流速测量分析领域,能够实时测量流体流速,准确分析管道中流体流量的变化。针对传统的超声波流量计功耗高、电路复杂的缺点,根据超声波时差法测量流量的原理,结合井下高温测量环境,以及未来测井仪器低功耗、小型化的需求,以dsPIC33EV为主控芯片,设计了一种低功耗、小型化的井下超声波流量测量系统。该系统利用dsPIC33EV的充电时间测量单元CTMU(Charging Time Measurement Unit),实现声波传播时差与流量的高精度测量与计算。室内实验平台测试数据表明,该文设计的井下超声波流量测量系统测量相对误差为±7.2%,典型功耗为20mW,技术指标满足生产井流量监测需求。展开更多
How to fit a properly nonlinear classification model from conventional well logs to lithofacies is a key problem for machine learning methods.Kernel methods(e.g.,KFD,SVM,MSVM)are effective attempts to solve this issue...How to fit a properly nonlinear classification model from conventional well logs to lithofacies is a key problem for machine learning methods.Kernel methods(e.g.,KFD,SVM,MSVM)are effective attempts to solve this issue due to abilities of handling nonlinear features by kernel functions.Deep mining of log features indicating lithofacies still needs to be improved for kernel methods.Hence,this work employs deep neural networks to enhance the kernel principal component analysis(KPCA)method and proposes a deep kernel method(DKM)for lithofacies identification using well logs.DKM includes a feature extractor and a classifier.The feature extractor consists of a series of KPCA models arranged according to residual network structure.A gradient-free optimization method is introduced to automatically optimize parameters and structure in DKM,which can avoid complex tuning of parameters in models.To test the validation of the proposed DKM for lithofacies identification,an open-sourced dataset with seven con-ventional logs(GR,CAL,AC,DEN,CNL,LLD,and LLS)and lithofacies labels from the Daniudi Gas Field in China is used.There are eight lithofacies,namely clastic rocks(pebbly,coarse,medium,and fine sand-stone,siltstone,mudstone),coal,and carbonate rocks.The comparisons between DKM and three commonly used kernel methods(KFD,SVM,MSVM)show that(1)DKM(85.7%)outperforms SVM(77%),KFD(79.5%),and MSVM(82.8%)in accuracy of lithofacies identification;(2)DKM is about twice faster than the multi-kernel method(MSVM)with good accuracy.The blind well test in Well D13 indicates that compared with the other three methods DKM improves about 24%in accuracy,35%in precision,41%in recall,and 40%in F1 score,respectively.In general,DKM is an effective method for complex lithofacies identification.This work also discussed the optimal structure and classifier for DKM.Experimental re-sults show that(m_(1),m_(2),O)is the optimal model structure and linear svM is the optimal classifier.(m_(1),m_(2),O)means there are m KPCAs,and then m2 residual units.A workflow to determine an optimal classifier in DKM for lithofacies identification is proposed,too.展开更多
基金supported by the Integrated Rail Transit Dispatch Control and Intermodal Transport Service Technology Project(Grant No.2022YFB4300500).
文摘The Balise Transmission Module(BTM)unit of the on-board train control system is a crucial component.Due to its unique installation position and complex environment,this unit has a higher fault rate within the on-board train control system.To conduct fault prediction for the BTM unit based on actual fault data,this study proposes a prediction method combining reliability statistics and machine learning,and achieves the fusion of prediction results from different dimensions through multi-method interactive validation.Firstly,a method for predicting equipment fault time targeting batch equipment is introduced.This method utilizes reliability statistics to construct a model of the remaining faultless operating time distribution considering uncertainty,thereby predicting the remaining faultless operating probability of the BTM unit.Secondly,considering the complexity of the BTM unit’s fault mechanism,the small sample size of fault cases,and the potential presence of multiple fault features in fault text records,an individual-oriented fault prediction method based on Bayesian-optimized Gradient Boosting Regression Tree(Bayes-GBRT)is proposed.This method achieves better prediction results compared to linear regression algorithms and random forest regression algorithms,with an average absolute error of only 0.224 years for predicting the fault time of this type of equipment.Finally,a multi-method interactive validation approach is proposed,enabling the fusion and validation of multi-dimensional results.The results indicate that the predicted fault time and the actual fault time conform to a log-normal distribution,and the parameter estimation results are basically consistent,verifying the accuracy and effectiveness of the prediction results.The above research findings can provide technical support for the maintenance and modification of BTM units,effectively reducing maintenance costs and ensuring the safe operation of high-speed railway,thus having practical engineering value for preventive maintenance.
基金Supported by the National 863 Plan Foundation under Grant No.2003AA414060
文摘In the process of designing self-elevating drilling unit, it is important, yet complicated, to use comparison and filtering to select the optimum scheme from the feasible ones. In this research, an index system and methodology for the evaluation of self-elevating drilling unit was proposed. Based on this, a multi-objective combinatorial optimization model was developed, using the improved grey relation Analysis （GRA）, in which the analytic hierarchy process （AHP） was used to determine the weights of the evaluating indices. It considered the connections within the indices, reflecting the objective nature of things, and also considered the subjective interests of ship owners and the needs of designers. The evaluation index system and evaluation method can be used in the selection of an optimal scheme and advanced assessment. A case study shows the index system and evaluation method are scientific, reasonable, and easy to put into practice. At the same time, such an evaluation index system and evaluation method will be helpful for making decisions for other mobile platforms.
文摘Quality function deployment （QFD） is a quality system, that can help to design novel products that meet customers＇ needs. Theory of inventive problem solving （TRIZ） is a very powerful tool in helping to solve difficult technical problems encountered in the design process. Introducing QFD and TRIZ into the conceptual design of the pumping unit combines advantages of these two theories, therefore meeting different demands of different users. It can tell us “What should we do it” with QFD and “How should we do it” with TRIZ. The conceptual design method, which is based on QFD and TRIZ, is introduced andused to analyze and evaluate the conceptual design project of a pumping unit.
文摘Fluid-structure interaction （FSI） problems in microchannels play a prominent role in many engineering applications. The present study is an effort toward the simulation of flow in microchannel considering FSI. The bottom boundary of the microchannel is simulated by size-dependent beam elements for the finite element method （FEM） based on a modified cou- ple stress theory. The lattice Boltzmann method （LBM） using the D2Q13 LB model is coupled to the FEM in order to solve the fluid part of the FSI problem. Because of the fact that the LBM generally needs only nearest neighbor information, the algorithm is an ideal candidate for parallel computing. The simulations are carried out on graphics processing units （GPUs） using computed unified device architecture （CUDA）. In the present study, the governing equations are non-dimensionalized and the set of dimensionless groups is exhibited to show their effects on micro-beam displacement. The numerical results show that the displacements of the micro-beam predicted by the size-dependent beam element are smaller than those by the classical beam element.
文摘The flow stress of ferrite/pearlite steel under uni-axial tension was simulated with finite element method (FEM) by applying commercial software MARC/MENTAT. Flow stress curves of ferrite/pearlite steels were calculated based on unit cell model. The effects of volume fraction, distribution and the aspect ratio of pearlite on tensile properties have been investigated.
文摘Aiming at that the successive test data set of the strapdown inertial measurement unit is always small,a Bayesian method is used to study its statistical characteristics.Its prior and posterior distributions are set up by the method and the pretest,sample and population information.Some statistical inferences can be made based on the posterior distribution.It can reduce the statistical analysis error in the case of small sample set.
文摘得益于日趋完善的状态监测系统,抽水蓄能电站记录了海量机组运行信息,为开展抽蓄机组劣化趋势评估提供了可靠的数据保障。为此,提出了一种基于Optuna-CatBoost和CRITIC(Criteria Importance though Intercrieria Correlation)评价法的水电机组劣化评估方法。首先利用最大互信息系数(Maximal Information Coefficient)筛选出机组关键工况系数;然后利用Optuna对CatBoost进行参数寻优,建立Optuna-CatBoost水电机组劣化趋势评估模型;最后基于CRITIC评价法对各通道劣化序列客观赋权,生成机组整机劣化序列。试验结果表明,所提模型的精度优于其他对比模型,能很好地反映机组整机劣化趋势。
文摘基于超声波时差测量流量的方法具有非接触、易安装、不改变流体的运动状态等优点,被广泛应用于油田井下流体流速测量分析领域,能够实时测量流体流速,准确分析管道中流体流量的变化。针对传统的超声波流量计功耗高、电路复杂的缺点,根据超声波时差法测量流量的原理,结合井下高温测量环境,以及未来测井仪器低功耗、小型化的需求,以dsPIC33EV为主控芯片,设计了一种低功耗、小型化的井下超声波流量测量系统。该系统利用dsPIC33EV的充电时间测量单元CTMU(Charging Time Measurement Unit),实现声波传播时差与流量的高精度测量与计算。室内实验平台测试数据表明,该文设计的井下超声波流量测量系统测量相对误差为±7.2%,典型功耗为20mW,技术指标满足生产井流量监测需求。
基金supported by the National Natural Science Foundation of China(Grant No.42002134)China Postdoctoral Science Foundation(Grant No.2021T140735)Science Foundation of China University of Petroleum,Beijing(Grant Nos.2462020XKJS02 and 2462020YXZZ004).
文摘How to fit a properly nonlinear classification model from conventional well logs to lithofacies is a key problem for machine learning methods.Kernel methods(e.g.,KFD,SVM,MSVM)are effective attempts to solve this issue due to abilities of handling nonlinear features by kernel functions.Deep mining of log features indicating lithofacies still needs to be improved for kernel methods.Hence,this work employs deep neural networks to enhance the kernel principal component analysis(KPCA)method and proposes a deep kernel method(DKM)for lithofacies identification using well logs.DKM includes a feature extractor and a classifier.The feature extractor consists of a series of KPCA models arranged according to residual network structure.A gradient-free optimization method is introduced to automatically optimize parameters and structure in DKM,which can avoid complex tuning of parameters in models.To test the validation of the proposed DKM for lithofacies identification,an open-sourced dataset with seven con-ventional logs(GR,CAL,AC,DEN,CNL,LLD,and LLS)and lithofacies labels from the Daniudi Gas Field in China is used.There are eight lithofacies,namely clastic rocks(pebbly,coarse,medium,and fine sand-stone,siltstone,mudstone),coal,and carbonate rocks.The comparisons between DKM and three commonly used kernel methods(KFD,SVM,MSVM)show that(1)DKM(85.7%)outperforms SVM(77%),KFD(79.5%),and MSVM(82.8%)in accuracy of lithofacies identification;(2)DKM is about twice faster than the multi-kernel method(MSVM)with good accuracy.The blind well test in Well D13 indicates that compared with the other three methods DKM improves about 24%in accuracy,35%in precision,41%in recall,and 40%in F1 score,respectively.In general,DKM is an effective method for complex lithofacies identification.This work also discussed the optimal structure and classifier for DKM.Experimental re-sults show that(m_(1),m_(2),O)is the optimal model structure and linear svM is the optimal classifier.(m_(1),m_(2),O)means there are m KPCAs,and then m2 residual units.A workflow to determine an optimal classifier in DKM for lithofacies identification is proposed,too.
