The recent studies on Artificial Intelligence(AI)accompanied by enhanced computing capabilities supports increasing attention into traditional control methods coupled with AI learning methods in an attempt to bringing...The recent studies on Artificial Intelligence(AI)accompanied by enhanced computing capabilities supports increasing attention into traditional control methods coupled with AI learning methods in an attempt to bringing adap-tiveness and fast responding features.The Model Predictive Control(MPC)tech-nique is a widely used,safe and reliable control method based on constraints.On the other hand,the Eddy Current dynamometers are highly nonlinear braking sys-tems whose performance parameters are related to many processes related vari-ables.This study is based on an adaptive model predictive control that utilizes selected AI methods.The presented approach presents an updated the mathema-tical model of an Eddy Current Dynamometer based on experimentally obtained system operational data.Finally,the comparison of AI methods and related learn-ing performances based on the assessment technique of mean absolute percentage error(MAPE)issues are discussed.The results indicate that Single Hidden Layer Neural Network(SHLNN),General Regression Neural Network(GRNN),Radial Basis Network(RBNN),Neuro Fuzzy Network(ANFIS)coupled MPC have quite satisfying performances.The presented results indicate that,amongst them,GRNN appears to provide the best performance.展开更多
AC motors, especially the squirrel cage induction motors have the advantages of simple structure, good reliability and low cost. They are more suitable to be used as electrical dynamometers to provide dynamic load for...AC motors, especially the squirrel cage induction motors have the advantages of simple structure, good reliability and low cost. They are more suitable to be used as electrical dynamometers to provide dynamic load for bench test systems. But, the speed and torque of induction motors are not easy to be controlled accurately. In this work, an electrical dynamometer based on the induction motor is proposed. In order to get better control performance of torque and speed of induction motor, an improved direct torque control method(DTC) is also developed based on the space vector modulation(SVM) technique. The performance of the proposed dynamometer system is validated in the Matlab/Simulink platform. The simulation results show that the new dynamometer has good torque and stator flux response. And the torque and stator current ripples of it are reduced significantly compared with using the conventional DTC method.展开更多
Background: Test-retest strength reliability of the Electronic Push/Pull Dynamometer (EPPD) in the measurement of the extensor and flexor muscles on a new constructed chair. The objective of the study was to assess re...Background: Test-retest strength reliability of the Electronic Push/Pull Dynamometer (EPPD) in the measurement of the extensor and flexor muscles on a new constructed chair. The objective of the study was to assess reliability of Electronic Push/Pull Dynamometer in the measurement of the knee flexion and extension at 90° and 60° on a new constructed chair. The aims of the author: To assess reliability of Electronic Push/Pull Dynamometer in the measurement of the knee flexion and extension at 90° and 60° on a new constructed chair. Design: A test-retest reliability study. Subjects: One hundred healthy students male and female (mean age, 21y). Methods: Maximum isometric strength of the quadriceps and hamstring muscle groups was measured using the EPPD were recorded at 60° and 90° for 3 trials on 2 occasions. Reliability was assessed with the Intraclass correlation coefficient (ICC), mean and standard deviation (SD) of measurements, and smallest real differences were calculated for the maximum and for the mean and work of the 3 repetitions. Results: Mean strength ranged from 50.44 kg for knee flexion to 55.76 kg for knee extension 50.44 kg to 61.98 kg at 90° hip flexion. Test-retest reliability Intraclass correlation coefficients (ICCs) ranged from 0.85 to 0.99. ICCs for test-retest reliability ranged from 0.780 to 0.998. Conclusions: The results of the reliability study indicate that the EPPD in reliable dynamometer to use in determining lower limb muscle force production. It can be used to measure disease progression and to evaluate changes in knee extension and flexion strength at the individual patient level.展开更多
DIT has competed for a number of years in the Formula Student competition with petrol engine propelled vehicles.Dynamometer testing on these engines was traditionally outsourced.In2017/2018a decision was made to comme...DIT has competed for a number of years in the Formula Student competition with petrol engine propelled vehicles.Dynamometer testing on these engines was traditionally outsourced.In2017/2018a decision was made to commence the design of an electric vehicle.Access to a suitable dynamometer for regular testing became more important than ever in order to fully characterise the electric motors the gearbox combinations and optimise the performance of the formula student electric vehicle.This paper deals with the design and manufacture and component selection for a mobile dynamometer that can accurately simulate racetrack conditions and apply typical loading cycles to a motor producing torque,power and speed values from a typical drivetrain.The dynamometer described uses a particle brake to apply the loads,a datum M425torque transducer to measure torque and a National Instruments LabVIEW interface to display and store data during testing.Safety is of vital importance and this paper describes the high-safety standards applied during the design and manufacture phase.With the implementation of an electronic control circuit,motor characteristics charts are produced,analysed and utilised for calibration and benchmarking for future motor test runs.Aim:Design&Manufacture of a mobile dynamometer unit to produce torque,speed and power values from the drive train of a motor.展开更多
High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an eff...High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis method.However,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational effort.Additionally,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different moments.Thus,there is heterogeneity in field data,which can dramatically impair the diagnostic accuracy.To solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this paper.First,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC data.Subsequently,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data heterogeneity.Finally,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the 4S-TFSM.Experiments on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational cost.To the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.展开更多
文摘The recent studies on Artificial Intelligence(AI)accompanied by enhanced computing capabilities supports increasing attention into traditional control methods coupled with AI learning methods in an attempt to bringing adap-tiveness and fast responding features.The Model Predictive Control(MPC)tech-nique is a widely used,safe and reliable control method based on constraints.On the other hand,the Eddy Current dynamometers are highly nonlinear braking sys-tems whose performance parameters are related to many processes related vari-ables.This study is based on an adaptive model predictive control that utilizes selected AI methods.The presented approach presents an updated the mathema-tical model of an Eddy Current Dynamometer based on experimentally obtained system operational data.Finally,the comparison of AI methods and related learn-ing performances based on the assessment technique of mean absolute percentage error(MAPE)issues are discussed.The results indicate that Single Hidden Layer Neural Network(SHLNN),General Regression Neural Network(GRNN),Radial Basis Network(RBNN),Neuro Fuzzy Network(ANFIS)coupled MPC have quite satisfying performances.The presented results indicate that,amongst them,GRNN appears to provide the best performance.
基金Project(SS2012AA04104)supported by High-tech Research and Development Program of China
文摘AC motors, especially the squirrel cage induction motors have the advantages of simple structure, good reliability and low cost. They are more suitable to be used as electrical dynamometers to provide dynamic load for bench test systems. But, the speed and torque of induction motors are not easy to be controlled accurately. In this work, an electrical dynamometer based on the induction motor is proposed. In order to get better control performance of torque and speed of induction motor, an improved direct torque control method(DTC) is also developed based on the space vector modulation(SVM) technique. The performance of the proposed dynamometer system is validated in the Matlab/Simulink platform. The simulation results show that the new dynamometer has good torque and stator flux response. And the torque and stator current ripples of it are reduced significantly compared with using the conventional DTC method.
文摘Background: Test-retest strength reliability of the Electronic Push/Pull Dynamometer (EPPD) in the measurement of the extensor and flexor muscles on a new constructed chair. The objective of the study was to assess reliability of Electronic Push/Pull Dynamometer in the measurement of the knee flexion and extension at 90° and 60° on a new constructed chair. The aims of the author: To assess reliability of Electronic Push/Pull Dynamometer in the measurement of the knee flexion and extension at 90° and 60° on a new constructed chair. Design: A test-retest reliability study. Subjects: One hundred healthy students male and female (mean age, 21y). Methods: Maximum isometric strength of the quadriceps and hamstring muscle groups was measured using the EPPD were recorded at 60° and 90° for 3 trials on 2 occasions. Reliability was assessed with the Intraclass correlation coefficient (ICC), mean and standard deviation (SD) of measurements, and smallest real differences were calculated for the maximum and for the mean and work of the 3 repetitions. Results: Mean strength ranged from 50.44 kg for knee flexion to 55.76 kg for knee extension 50.44 kg to 61.98 kg at 90° hip flexion. Test-retest reliability Intraclass correlation coefficients (ICCs) ranged from 0.85 to 0.99. ICCs for test-retest reliability ranged from 0.780 to 0.998. Conclusions: The results of the reliability study indicate that the EPPD in reliable dynamometer to use in determining lower limb muscle force production. It can be used to measure disease progression and to evaluate changes in knee extension and flexion strength at the individual patient level.
文摘DIT has competed for a number of years in the Formula Student competition with petrol engine propelled vehicles.Dynamometer testing on these engines was traditionally outsourced.In2017/2018a decision was made to commence the design of an electric vehicle.Access to a suitable dynamometer for regular testing became more important than ever in order to fully characterise the electric motors the gearbox combinations and optimise the performance of the formula student electric vehicle.This paper deals with the design and manufacture and component selection for a mobile dynamometer that can accurately simulate racetrack conditions and apply typical loading cycles to a motor producing torque,power and speed values from a typical drivetrain.The dynamometer described uses a particle brake to apply the loads,a datum M425torque transducer to measure torque and a National Instruments LabVIEW interface to display and store data during testing.Safety is of vital importance and this paper describes the high-safety standards applied during the design and manufacture phase.With the implementation of an electronic control circuit,motor characteristics charts are produced,analysed and utilised for calibration and benchmarking for future motor test runs.Aim:Design&Manufacture of a mobile dynamometer unit to produce torque,speed and power values from the drive train of a motor.
基金We would like to thank the associate editor and the reviewers for their constructive comments.This work was supported in part by the National Natural Science Foundation of China under Grant 62203234in part by the State Key Laboratory of Robotics of China under Grant 2023-Z03+1 种基金in part by the Natural Science Foundation of Liaoning Province under Grant 2023-BS-025in part by the Research Program of Liaoning Liaohe Laboratory under Grant LLL23ZZ-02-02.
文摘High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis method.However,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational effort.Additionally,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different moments.Thus,there is heterogeneity in field data,which can dramatically impair the diagnostic accuracy.To solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this paper.First,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC data.Subsequently,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data heterogeneity.Finally,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the 4S-TFSM.Experiments on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational cost.To the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.
