According to fault type diversity and fault information uncertainty problem of the hydraulic driven rocket launcher servo system（HDRLSS） , the fault diagnosis method based on the evidence theory and neural network e...According to fault type diversity and fault information uncertainty problem of the hydraulic driven rocket launcher servo system（HDRLSS） , the fault diagnosis method based on the evidence theory and neural network ensemble is proposed. In order to overcome the shortcomings of the single neural network, two improved neural network models are set up at the com-mon nodes to simplify the network structure. The initial fault diagnosis is based on the iron spectrum data and the pressure, flow and temperature（PFT） characteristic parameters as the input vectors of the two improved neural network models, and the diagnosis result is taken as the basic probability distribution of the evidence theory. Then the objectivity of assignment is real-ized. The initial diagnosis results of two improved neural networks are fused by D-S evidence theory. The experimental results show that this method can avoid the misdiagnosis of neural network recognition and improve the accuracy of the fault diagnosis of HDRLSS.展开更多
Purpose–The purpose of this paper is to accurately capture the risks which are caused by each road user in time.Design/methodology/approach–The authors proposed a novel risk assessment approach based on the multi-se...Purpose–The purpose of this paper is to accurately capture the risks which are caused by each road user in time.Design/methodology/approach–The authors proposed a novel risk assessment approach based on the multi-sensor fusion algorithm in the real traffic environment.Firstly,they proposed a novel detection-level fusion approach for multi-object perception in dense traffic environment based on evidence theory.This approach integrated four states of track life into a generic fusion framework to improve the performance of multi-object perception.The information of object type,position and velocity was accurately obtained.Then,they conducted several experiments in real dense traffic environment on highways and urban roads,which enabled them to propose a novel road traffic risk modeling approach based on the dynamic analysis of vehicles in a variety of driving scenarios.By analyzing the generation process of traffic risks between vehicles and the road environment,the equivalent forces of vehicle–vehicle and vehicle–road were presented and theoretically calculated.The prediction steering angle and trajectory were considered in the determination of traffic risk influence area.Findings–The results of multi-object perception in the experiments showed that the proposed fusion approach achieved low false and missing tracking,and the road traffic risk was described as afield of equivalent force.The results extend the understanding of the traffic risk,which supported that the traffic risk from the front and back of the vehicle can be perceived in advance.Originality/value–This approach integrated four states of track life into a generic fusion framework to improve the performance of multi-object perception.The information of object type,position and velocity was used to reduce erroneous data association between tracks and detections.Then,the authors conducted several experiments in real dense traffic environment on highways and urban roads,which enabled them to propose a novel road traffic risk modeling approach based on the dynamic analysis of vehicles in a variety of driving scenarios.By analyzing the generation process of traffic risks between vehicles and the road environment,the equivalent forces of vehicle–vehicle and vehicle–road were presented and theoretically calculated.展开更多
The displacement of transmission tower feet can seriously affect the safe operation of the tower,and the accuracy of structural health monitoring methods is limited at the present stage.The application of deep learnin...The displacement of transmission tower feet can seriously affect the safe operation of the tower,and the accuracy of structural health monitoring methods is limited at the present stage.The application of deep learning method provides new ideas for structural health monitoring of towers,but the current amount of tower vibration fault data is restricted to provide adequate training data for Deep Learning(DL).In this paper,we propose a DT-DL based tower foot displacement monitoring method,which firstly simulates the wind-induced vibration response data of the tower under each fault condition by finite element method.Then the vibration signal visualization and Data Transfer(DT)are used to add tower fault data samples to solve the problem of insufficient actual data quantity.Subsequently,the dynamic response test is carried out under different tower fault states,and the tower fault monitoring is carried out by the DL method.Finally,the proposed method is compared with the traditional online monitoring method,and it is found that this method can significantly improve the rate of convergence and recognition accuracy in the recognition process.The results show that the method can effectively identify the tower foot displacement state,which can greatly reduce the accidents that occurred due to the tower foot displacement.展开更多
文摘According to fault type diversity and fault information uncertainty problem of the hydraulic driven rocket launcher servo system（HDRLSS） , the fault diagnosis method based on the evidence theory and neural network ensemble is proposed. In order to overcome the shortcomings of the single neural network, two improved neural network models are set up at the com-mon nodes to simplify the network structure. The initial fault diagnosis is based on the iron spectrum data and the pressure, flow and temperature（PFT） characteristic parameters as the input vectors of the two improved neural network models, and the diagnosis result is taken as the basic probability distribution of the evidence theory. Then the objectivity of assignment is real-ized. The initial diagnosis results of two improved neural networks are fused by D-S evidence theory. The experimental results show that this method can avoid the misdiagnosis of neural network recognition and improve the accuracy of the fault diagnosis of HDRLSS.
基金supported by the National Science Fund for Distinguished Young Scholars(51625503)the National Natural Science Foundation of China,the General Project(51475254)the Major Project(61790561).
文摘Purpose–The purpose of this paper is to accurately capture the risks which are caused by each road user in time.Design/methodology/approach–The authors proposed a novel risk assessment approach based on the multi-sensor fusion algorithm in the real traffic environment.Firstly,they proposed a novel detection-level fusion approach for multi-object perception in dense traffic environment based on evidence theory.This approach integrated four states of track life into a generic fusion framework to improve the performance of multi-object perception.The information of object type,position and velocity was accurately obtained.Then,they conducted several experiments in real dense traffic environment on highways and urban roads,which enabled them to propose a novel road traffic risk modeling approach based on the dynamic analysis of vehicles in a variety of driving scenarios.By analyzing the generation process of traffic risks between vehicles and the road environment,the equivalent forces of vehicle–vehicle and vehicle–road were presented and theoretically calculated.The prediction steering angle and trajectory were considered in the determination of traffic risk influence area.Findings–The results of multi-object perception in the experiments showed that the proposed fusion approach achieved low false and missing tracking,and the road traffic risk was described as afield of equivalent force.The results extend the understanding of the traffic risk,which supported that the traffic risk from the front and back of the vehicle can be perceived in advance.Originality/value–This approach integrated four states of track life into a generic fusion framework to improve the performance of multi-object perception.The information of object type,position and velocity was used to reduce erroneous data association between tracks and detections.Then,the authors conducted several experiments in real dense traffic environment on highways and urban roads,which enabled them to propose a novel road traffic risk modeling approach based on the dynamic analysis of vehicles in a variety of driving scenarios.By analyzing the generation process of traffic risks between vehicles and the road environment,the equivalent forces of vehicle–vehicle and vehicle–road were presented and theoretically calculated.
基金supported by the Key Projects of Shaanxi Province Key R&D Program(2018ZDXM-GY-040)supported by Natural Science Foundation of Shaanxi Province,Basic Research Program Project(2019JQ-843)supported by Graduate Scientific Innovation Fund for Xi’an Polytechnic University(chx2023012).
文摘The displacement of transmission tower feet can seriously affect the safe operation of the tower,and the accuracy of structural health monitoring methods is limited at the present stage.The application of deep learning method provides new ideas for structural health monitoring of towers,but the current amount of tower vibration fault data is restricted to provide adequate training data for Deep Learning(DL).In this paper,we propose a DT-DL based tower foot displacement monitoring method,which firstly simulates the wind-induced vibration response data of the tower under each fault condition by finite element method.Then the vibration signal visualization and Data Transfer(DT)are used to add tower fault data samples to solve the problem of insufficient actual data quantity.Subsequently,the dynamic response test is carried out under different tower fault states,and the tower fault monitoring is carried out by the DL method.Finally,the proposed method is compared with the traditional online monitoring method,and it is found that this method can significantly improve the rate of convergence and recognition accuracy in the recognition process.The results show that the method can effectively identify the tower foot displacement state,which can greatly reduce the accidents that occurred due to the tower foot displacement.
