The machine tool equipped with the dual-drive servo feed system could realize high feed speed as well as sharp precision. Currently, there is no report about the thermal behaviors of the dual-drive machine, and the cu...The machine tool equipped with the dual-drive servo feed system could realize high feed speed as well as sharp precision. Currently, there is no report about the thermal behaviors of the dual-drive machine, and the current research of the thermal characteristics of machines mainly focuses on steady simulation. To explore the influence of thermal characterizations on the precision of a jib boring machine assembled dual-drive feed system, the thermal equilibrium tests and the research on thermal-mechanical transient behaviors are carried out. A laser interferometer, infrared thermography and a temperature-displacement acquisition system are applied to measure the temperature distribution and thermal deformation at different feed speeds. Subsequently, the finite element method （FEM） is used to analyze the transient thermal behaviors of the boring machine. The complex boundary conditions, such as heat sources and convective heat transfer coefficient, are calculated. Finally, transient variances in temperatures and deformations are compared with the measured values, and the errors between the measurement and the simulation of the temperature and the thermal error are 2 ~C and 2.5 pm, respectively. The researching results demonstrate that the FEM model can predict the thermal error and temperature distribution very well under specified operating condition. Moreover, the uneven temperature gradient is due to the asynchronous dual-drive structure that results in thermal deformation. Additionally, the positioning accuracy decreases as the measured point became further away from the motor, and the thermal error and equilibrium period both increase with feed speeds. The research proposes a systematical method to measure and simulate the boring machine transient thermal behaviors.展开更多
Distributed drive electric vehicles(DDEVs)possess great advantages in the viewpoint of fuel consumption,environment protection and traffic mobility.Whereas the effects of inertial parameter variation in DDEV control s...Distributed drive electric vehicles(DDEVs)possess great advantages in the viewpoint of fuel consumption,environment protection and traffic mobility.Whereas the effects of inertial parameter variation in DDEV control system become much more pronounced due to the drastic reduction of vehicle weights and body size,and inertial parameter has seldom been tackled and systematically estimated.This paper presents a dual central difference Kalman filter(DCDKF)where two Kalman filters run in parallel to simultaneously estimate vehicle different dynamic states and inertial parameters,such as vehicle sideslip angle,vehicle mass,vehicle yaw moment of inertia,the distance from the front axle to centre of gravity.The proposed estimation method only integrates and utilizes real-time measurements of hub torque information and other in-vehicle sensors from standard DDEVs.The four-wheel nonlinear vehicle dynamics estimation model considering payload variations,Pacejka tire model,wheel and motor dynamics model is developed,the observability of the DCDKF observer is analysed and derived via Lie derivative and differential geometry theory.To address system nonlinearities in vehicle dynamics estimation,the DCDKF and dual extended Kalman filter(DEKF)are also investigated and compared.Simulation with various maneuvers are carried out to verify the effectiveness of the proposed method using Matlab/Simulink-CarsimR.The results show that the proposed DCDKF method can effectively estimate vehicle dynamic states and inertial parameters despite the existence of payload variations and variable driving conditions.This research provides a boot-strapping procedure which can performs optimal estimation to estimate simultaneously vehicle system state and inertial parameter with high accuracy and real-time ability.展开更多
针对疲劳检测中小尺度检测效果不佳和实时性差等问题,以矿井提升机司机疲劳检测为目标,对YOLOv7的结构进行精简并且基于AIoU(Area Intersection over Union)损失函数优化预测框与验证框的回归过程.在模型中引入双通道注意力机制实现小...针对疲劳检测中小尺度检测效果不佳和实时性差等问题,以矿井提升机司机疲劳检测为目标,对YOLOv7的结构进行精简并且基于AIoU(Area Intersection over Union)损失函数优化预测框与验证框的回归过程.在模型中引入双通道注意力机制实现小尺度特征的信息增强,通过融合眨眼频率、闭眼时长和打哈欠时长来判断司机的状态.实验结果表明,本文方法对疲劳检测精度达到98.85%,检测速度达到70 FPS,与其他算法相比,本文算法具有更好的准确性和实时性.展开更多
基金Supported by National Hi-tech Research and Development Program of China(863 Program,Grant No.2012AA040701)
文摘The machine tool equipped with the dual-drive servo feed system could realize high feed speed as well as sharp precision. Currently, there is no report about the thermal behaviors of the dual-drive machine, and the current research of the thermal characteristics of machines mainly focuses on steady simulation. To explore the influence of thermal characterizations on the precision of a jib boring machine assembled dual-drive feed system, the thermal equilibrium tests and the research on thermal-mechanical transient behaviors are carried out. A laser interferometer, infrared thermography and a temperature-displacement acquisition system are applied to measure the temperature distribution and thermal deformation at different feed speeds. Subsequently, the finite element method （FEM） is used to analyze the transient thermal behaviors of the boring machine. The complex boundary conditions, such as heat sources and convective heat transfer coefficient, are calculated. Finally, transient variances in temperatures and deformations are compared with the measured values, and the errors between the measurement and the simulation of the temperature and the thermal error are 2 ~C and 2.5 pm, respectively. The researching results demonstrate that the FEM model can predict the thermal error and temperature distribution very well under specified operating condition. Moreover, the uneven temperature gradient is due to the asynchronous dual-drive structure that results in thermal deformation. Additionally, the positioning accuracy decreases as the measured point became further away from the motor, and the thermal error and equilibrium period both increase with feed speeds. The research proposes a systematical method to measure and simulate the boring machine transient thermal behaviors.
基金Supported by National Natural Science Foundation of China(Grant Nos.51905329,51975118)Foundation of State Key Laboratory of Automotive Simulation and Control of China(Grant No.20181112).
文摘Distributed drive electric vehicles(DDEVs)possess great advantages in the viewpoint of fuel consumption,environment protection and traffic mobility.Whereas the effects of inertial parameter variation in DDEV control system become much more pronounced due to the drastic reduction of vehicle weights and body size,and inertial parameter has seldom been tackled and systematically estimated.This paper presents a dual central difference Kalman filter(DCDKF)where two Kalman filters run in parallel to simultaneously estimate vehicle different dynamic states and inertial parameters,such as vehicle sideslip angle,vehicle mass,vehicle yaw moment of inertia,the distance from the front axle to centre of gravity.The proposed estimation method only integrates and utilizes real-time measurements of hub torque information and other in-vehicle sensors from standard DDEVs.The four-wheel nonlinear vehicle dynamics estimation model considering payload variations,Pacejka tire model,wheel and motor dynamics model is developed,the observability of the DCDKF observer is analysed and derived via Lie derivative and differential geometry theory.To address system nonlinearities in vehicle dynamics estimation,the DCDKF and dual extended Kalman filter(DEKF)are also investigated and compared.Simulation with various maneuvers are carried out to verify the effectiveness of the proposed method using Matlab/Simulink-CarsimR.The results show that the proposed DCDKF method can effectively estimate vehicle dynamic states and inertial parameters despite the existence of payload variations and variable driving conditions.This research provides a boot-strapping procedure which can performs optimal estimation to estimate simultaneously vehicle system state and inertial parameter with high accuracy and real-time ability.
文摘针对疲劳检测中小尺度检测效果不佳和实时性差等问题,以矿井提升机司机疲劳检测为目标,对YOLOv7的结构进行精简并且基于AIoU(Area Intersection over Union)损失函数优化预测框与验证框的回归过程.在模型中引入双通道注意力机制实现小尺度特征的信息增强,通过融合眨眼频率、闭眼时长和打哈欠时长来判断司机的状态.实验结果表明,本文方法对疲劳检测精度达到98.85%,检测速度达到70 FPS,与其他算法相比,本文算法具有更好的准确性和实时性.
