在NMGH墨西哥叉车车架厂,由于采用了ESAB OK AristoRod无铜MAG焊丝,从而降低了成本并提高了生产效率。ESAB OK AristoRod无铜焊丝投放到市场仅仅5年,目前在世界范围内被各行各业广泛接受。本文是来自ESAB公司墨西哥NMHG报道,称使用该类...在NMGH墨西哥叉车车架厂,由于采用了ESAB OK AristoRod无铜MAG焊丝,从而降低了成本并提高了生产效率。ESAB OK AristoRod无铜焊丝投放到市场仅仅5年,目前在世界范围内被各行各业广泛接受。本文是来自ESAB公司墨西哥NMHG报道,称使用该类焊丝客户受益匪浅。展开更多
Vibration failure in the pumping system is a significant issue for indus-tries that rely on the pump as a critical device which requires regular maintenance.To save energy and money,a new automated system must be devel...Vibration failure in the pumping system is a significant issue for indus-tries that rely on the pump as a critical device which requires regular maintenance.To save energy and money,a new automated system must be developed that can detect anomalies at an early stage.This paper presents a case study of a machine learning(ML)-based computational technique for automatic fault detection in a cascade pumping system based on variable frequency drive(VFD).Since the intensity of the vibrational effect depends on which axis has the most significant effect,a three-axis accelerometer is used to measure it in the pumping system.The emphasis is on determining the vibration effect on different axes.For experiment,various ML algorithms are investigated on collected vibratory data through Matlab software in x,y,z axes and performances of the algorithms are compared based on accuracy rate,prediction speed and training time.Based on the proposed research results,the multiclass support vector machine(MSVM)is found to be the best suitable algorithm compared to other algorithms.It has been demonstrated that ML algorithms can detect faults automatically rather than conventional meth-ods.MSVM is used for the proposed work because it is less complex and pro-duces better results with a limited data set.展开更多
This paper describes a supervisory hierarchical fuzzy controller （SHFC） for regulating pressure in a real-time pilot pressure control system. The input scaling factor tuning of a direct expert controller is made usi...This paper describes a supervisory hierarchical fuzzy controller （SHFC） for regulating pressure in a real-time pilot pressure control system. The input scaling factor tuning of a direct expert controller is made using the error and process input parameters in a closed loop system in order to obtain better controller performance for set-point change and load disturbances. This on-line tuning method reduces operator involvement and enhances the controller performance to a wide operating range. The hierarchical control scheme consists of an intelligent upper level supervisory fuzzy controller and a lower level direct fuzzy controller. The upper level controller provides a mechanism to the main goal of the system and the lower level controller delivers the solutions to a particular situation. The control algorithm for the proposed scheme has been developed and tested using an ARM7 microcontroller-based embedded target board for a nonlinear pressure process having dead time. To demonstrate the effectiveness, the results of the proposed hierarchical controller, fuzzy controller and conventional proportional-integral （PI） controller are analyzed. The results prove that the SHFC performance is better in terms of stability and robustness than the conventional control methods.展开更多
ESAB GEM GLOBAL CUTTNG TECHNOLOGYGROUP作为世界上最大的焊接和切割设备供应商ESAB的独立分支机构，秉持“Come Grow With Us”的宗旨为其合作伙伴提供支持，助其成功。公司将为客户提供多种高性能组件，其中的m^3plasma^TM能够在高...ESAB GEM GLOBAL CUTTNG TECHNOLOGYGROUP作为世界上最大的焊接和切割设备供应商ESAB的独立分支机构，秉持“Come Grow With Us”的宗旨为其合作伙伴提供支持，助其成功。公司将为客户提供多种高性能组件，其中的m^3plasma^TM能够在高切割速度下实现完美的切割质量。展开更多
The advancement in technologies made the entire manufacturing system,to be operated with more efficient,flexible,user friendly,more productive and cost effective.One such a system to be focused for advancement is plas...The advancement in technologies made the entire manufacturing system,to be operated with more efficient,flexible,user friendly,more productive and cost effective.One such a system to be focused for advancement is plasma cutting system,which has wider industrial applications.There are many researches pursuing at various area of plasma cutting technology,still the automated and optimized parameters value selection is challenging.The work is aimed to eliminate the manual mode of feeding the input parameters for cutting operation.At present,cutting parameters are fed by referring the past cut data information or with the assistance of experienced employers.The cutting process parameters selections will have direct impact on the quality of the material being cut,and life of the consumables.This paper is intended to automate the process parameters selection by developing the mathematical model with existing cutting process parameters database.In this,three different approaches,multiple regression,multiple polynomial regression and AI technique,are selected and analyzed with the mathematical relations developed between the different cutting process parameters.The accuracy and reliability of those methods are detailed.The advantage and disadvantage of those methods for optimal setting conditions are discussed.The appropriate method that can be preferred for automated and optimal settings are elucidated.Finally,the selected technique is checked for accuracy and reliability for the existing cut data.展开更多
文摘Vibration failure in the pumping system is a significant issue for indus-tries that rely on the pump as a critical device which requires regular maintenance.To save energy and money,a new automated system must be developed that can detect anomalies at an early stage.This paper presents a case study of a machine learning(ML)-based computational technique for automatic fault detection in a cascade pumping system based on variable frequency drive(VFD).Since the intensity of the vibrational effect depends on which axis has the most significant effect,a three-axis accelerometer is used to measure it in the pumping system.The emphasis is on determining the vibration effect on different axes.For experiment,various ML algorithms are investigated on collected vibratory data through Matlab software in x,y,z axes and performances of the algorithms are compared based on accuracy rate,prediction speed and training time.Based on the proposed research results,the multiclass support vector machine(MSVM)is found to be the best suitable algorithm compared to other algorithms.It has been demonstrated that ML algorithms can detect faults automatically rather than conventional meth-ods.MSVM is used for the proposed work because it is less complex and pro-duces better results with a limited data set.
文摘This paper describes a supervisory hierarchical fuzzy controller （SHFC） for regulating pressure in a real-time pilot pressure control system. The input scaling factor tuning of a direct expert controller is made using the error and process input parameters in a closed loop system in order to obtain better controller performance for set-point change and load disturbances. This on-line tuning method reduces operator involvement and enhances the controller performance to a wide operating range. The hierarchical control scheme consists of an intelligent upper level supervisory fuzzy controller and a lower level direct fuzzy controller. The upper level controller provides a mechanism to the main goal of the system and the lower level controller delivers the solutions to a particular situation. The control algorithm for the proposed scheme has been developed and tested using an ARM7 microcontroller-based embedded target board for a nonlinear pressure process having dead time. To demonstrate the effectiveness, the results of the proposed hierarchical controller, fuzzy controller and conventional proportional-integral （PI） controller are analyzed. The results prove that the SHFC performance is better in terms of stability and robustness than the conventional control methods.
文摘ESAB GEM GLOBAL CUTTNG TECHNOLOGYGROUP作为世界上最大的焊接和切割设备供应商ESAB的独立分支机构，秉持“Come Grow With Us”的宗旨为其合作伙伴提供支持，助其成功。公司将为客户提供多种高性能组件，其中的m^3plasma^TM能够在高切割速度下实现完美的切割质量。
文摘The advancement in technologies made the entire manufacturing system,to be operated with more efficient,flexible,user friendly,more productive and cost effective.One such a system to be focused for advancement is plasma cutting system,which has wider industrial applications.There are many researches pursuing at various area of plasma cutting technology,still the automated and optimized parameters value selection is challenging.The work is aimed to eliminate the manual mode of feeding the input parameters for cutting operation.At present,cutting parameters are fed by referring the past cut data information or with the assistance of experienced employers.The cutting process parameters selections will have direct impact on the quality of the material being cut,and life of the consumables.This paper is intended to automate the process parameters selection by developing the mathematical model with existing cutting process parameters database.In this,three different approaches,multiple regression,multiple polynomial regression and AI technique,are selected and analyzed with the mathematical relations developed between the different cutting process parameters.The accuracy and reliability of those methods are detailed.The advantage and disadvantage of those methods for optimal setting conditions are discussed.The appropriate method that can be preferred for automated and optimal settings are elucidated.Finally,the selected technique is checked for accuracy and reliability for the existing cut data.
