基于车辆动态响应的轨道不平顺智能感知算法研究
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摘要
轨道交通是我国最重要的交通运输方式,是国民经济和社会发展的命脉。随着列车运行速度、运载重量和运输密度的大幅度提高,车辆与轨道结构之间的动态相互作用日益增强,导致车辆对线路结构的动力破坏作用也越严重,而线路状态的恶化反过来影响列车运行的安全性和乘坐舒适性,因此及时掌握线路状态对于保证列车运行安全和合理制定维修计划具有重要意义。本文以轨道不平顺为研究对象,结合车辆轨道耦合模型,研究了一种基于车辆振动动态响应的轨道不平顺智能感知算法,能够实现运营列车以不同速度通过线路时轨道不平顺的获取,弥补了轨检车或综合检测车检测周期内轨道不平顺状态无法获知的不足,对于保证行车安全、线路设计、养护维修等具有重要的理论意义和现实意义。
论文首先在已有车辆轨道耦合模型的基础上,仿真分析了不同线路(不同轨道不平顺)、不同列车运行速度、不同车辆类型作用下车辆轨道动态振动特性,进一步明确了论文的研究意义,同时为后面轨道几何不平顺的估计提供了仿真数据。
为实现利用运营车辆的车辆振动响应感知轨道几何静态不平顺,论文提出了一种基于微种群遗传算法和车辆轨道耦合模型相结合的轨道几何静态不平顺估计算法,将轨道几何静态不平顺看做车辆轨道耦合模型非线性系统的一个参数,进而将轨道不平顺求解转换为模型参数的估计。参数估计准则采用车辆轨道耦合模型与车辆测量模型输出之差平方和最小,利用遗传算法在解空间内直接搜索轨道静态不平顺的最优解。由于车辆轨道耦合模型动力学方程为大型非线性方程组,为缩短计算时间和计算量,论文研究了改进的微种群遗传算法,撇弃了一般微种群遗传算法中的重启步骤,增大了变异概率,在进化过程中使用了最优保留策略,仿真计算结果表明估计的轨道几何静态不平顺在检测误差允许范围内。
轨道基础结构缺陷引起的刚度突变是造成轨道刚度不平顺的主要原因,在列车通过时会引起较大的动态不平顺。论文在分析了轨道基础结构缺陷下车辆轨道动态响应的基础上,提出了一种基于支持向量机和车辆振动响应进行轨道刚度突变的识别算法,为了提高轨道刚度不平顺的识别率和分类准确性,在支持向量机的参数选择上,使用了粒子群算法和遗传算法,设计了改进的支持向量机,通过仿真分析表明这两种算法对于提高分类准确率和缩短计算时间都十分有效,在轨道基础结构缺陷的识别中取得了良好效果,同时为构建真实的轨道模型进行轨道动态不平顺估计奠定了基础。
由于测量传感器和理论模型都存在误差,为了提高轨道不平顺的估计精度,论文最后提出了一种基于遗传算法和无迹卡尔曼滤波(UKF)技术相嵌套的轨道动态不平顺优化算法。借助车辆轨道耦合模型、测量传感器的已知特性,优化轨道振动响应,提高轨道动态不平顺的估计精度。仿真结果表明,经过GA与UKF嵌套算法后的轨道几何静态不平顺、车辆、轨道振动响应精度都得到了提高,减小了轨道动态不平顺估计误差。论文研究结果实现了根据车辆振动响应估计不同运营列车不同速度通过时的轨道动态不平顺,为实现车对地的检测提供了一种新的解决方案。
Railway transportation is the most important traffic mode in China and is the lifeblood of the national economy and social development. With the substantial increase of train speed, carrying weight and traffic density, the dynamic interaction between vehicle and track is increasing. Damaging effects of vehicle on track is more serious, which even influence the train operation safety and ride comfort. Hence, it is of great significance to master the track status for ensuring the train operation safety and developing the maintenance plan. Track irregularity is studied in this dissertation, and an intelligent sensing method for track irregularity is proposed based on vehicle dynamic vibration responses, which combines with vehicle and track coupling model. Track dynamic irregularity can be acquired by this method when operating trains go through at the different speed. It can make up the deficiencies that track irregularity cannot be informed during the inspection cars detection cycle. So it has important theoretical and practical meaning for the train operation safety, track design and maintenance.
Firstly, vehicle and track vibration characteristics are analyzed based on vehicle-track coupling system model under some kinds of conditions, such as different track condition, different speed, different vehicle types and different track irregularity. The simulation results further show the research significance and provide the data for the latter track irregularity estimation.
A new algorithm based on micro genetic algorithm and vehicle-track coupling system model is proposed in the dissertation, which can acquire the track irregularity using the vehicle dynamic responses of operation train. In the algorithm track static irregularity is taken as an unknown parameter of nonlinear vehicle-track coupling system model, and then the solution of track irregularity is converted into a parameter estimation problem. Making the sum of error square between vehicle track model output and measurement value to be minimum is used as parameter estimation rule, and genetic algorithm is adopted to search the optimum solution in the solution space. Because the dynamic equations of vehicle-track coupling system are the large nonlinear equation set, an improved micro genetic algorithm is studied to reduce the computing time and workload. The restart step is removed from micro genetic algorithm and mutation operator is added. The optimal retention policy is adopted during the evolution process. The estimation results show the estimated track static irregularity error is in the allowable range.
Track stiffness abrupt change caused by track infrastructure defections is the main reason which creates track stiffness irregularity. It can lead to rather large dynamic irregularity when the train goes through it. The dynamic responses of vehicle and track are analyzed when defections exist in the track substructure, and a recognition algorithm of track rigidity abrupt change is proposed based on Support Vector Machines, in which vehicle vibration responses are used. In order to improve the recognition precision, improved SVM is designed by GA and PSO methods to optimize the parameters of SVM. The recognition results show that these two methods are effective to enhance the accuracy and reduce calculation time and get the better results in track substructure defects recognition. Meanwhile, it makes a foundation for the more realistic track model.
A nested algorithm based on GA and Unscented Kalman Filtering is investigated in the dissertation in order to improve the precision of track irregularity estimation, which is influenced by the error of measurement sensors and theoretical model. With the known characteristics of vehicle-track coupling system and measurement sensors, the dynamic responses of track are optimized to get higher estimation precision of track dynamic irregularity. The simulation results show that track static irregularity and all dynamic responses of vehicle and track are getting better after GA and UKF nested algorithm and the estimation error is decreased. Track dynamic irregularity, which operating trains go through at the different speed, can be estimated based on vehicle dynamic responses. The research in this dissertation provides a new solution to detection the track status from the vehicle devices.
论文首先在已有车辆轨道耦合模型的基础上,仿真分析了不同线路(不同轨道不平顺)、不同列车运行速度、不同车辆类型作用下车辆轨道动态振动特性,进一步明确了论文的研究意义,同时为后面轨道几何不平顺的估计提供了仿真数据。
为实现利用运营车辆的车辆振动响应感知轨道几何静态不平顺,论文提出了一种基于微种群遗传算法和车辆轨道耦合模型相结合的轨道几何静态不平顺估计算法,将轨道几何静态不平顺看做车辆轨道耦合模型非线性系统的一个参数,进而将轨道不平顺求解转换为模型参数的估计。参数估计准则采用车辆轨道耦合模型与车辆测量模型输出之差平方和最小,利用遗传算法在解空间内直接搜索轨道静态不平顺的最优解。由于车辆轨道耦合模型动力学方程为大型非线性方程组,为缩短计算时间和计算量,论文研究了改进的微种群遗传算法,撇弃了一般微种群遗传算法中的重启步骤,增大了变异概率,在进化过程中使用了最优保留策略,仿真计算结果表明估计的轨道几何静态不平顺在检测误差允许范围内。
轨道基础结构缺陷引起的刚度突变是造成轨道刚度不平顺的主要原因,在列车通过时会引起较大的动态不平顺。论文在分析了轨道基础结构缺陷下车辆轨道动态响应的基础上,提出了一种基于支持向量机和车辆振动响应进行轨道刚度突变的识别算法,为了提高轨道刚度不平顺的识别率和分类准确性,在支持向量机的参数选择上,使用了粒子群算法和遗传算法,设计了改进的支持向量机,通过仿真分析表明这两种算法对于提高分类准确率和缩短计算时间都十分有效,在轨道基础结构缺陷的识别中取得了良好效果,同时为构建真实的轨道模型进行轨道动态不平顺估计奠定了基础。
由于测量传感器和理论模型都存在误差,为了提高轨道不平顺的估计精度,论文最后提出了一种基于遗传算法和无迹卡尔曼滤波(UKF)技术相嵌套的轨道动态不平顺优化算法。借助车辆轨道耦合模型、测量传感器的已知特性,优化轨道振动响应,提高轨道动态不平顺的估计精度。仿真结果表明,经过GA与UKF嵌套算法后的轨道几何静态不平顺、车辆、轨道振动响应精度都得到了提高,减小了轨道动态不平顺估计误差。论文研究结果实现了根据车辆振动响应估计不同运营列车不同速度通过时的轨道动态不平顺,为实现车对地的检测提供了一种新的解决方案。
Railway transportation is the most important traffic mode in China and is the lifeblood of the national economy and social development. With the substantial increase of train speed, carrying weight and traffic density, the dynamic interaction between vehicle and track is increasing. Damaging effects of vehicle on track is more serious, which even influence the train operation safety and ride comfort. Hence, it is of great significance to master the track status for ensuring the train operation safety and developing the maintenance plan. Track irregularity is studied in this dissertation, and an intelligent sensing method for track irregularity is proposed based on vehicle dynamic vibration responses, which combines with vehicle and track coupling model. Track dynamic irregularity can be acquired by this method when operating trains go through at the different speed. It can make up the deficiencies that track irregularity cannot be informed during the inspection cars detection cycle. So it has important theoretical and practical meaning for the train operation safety, track design and maintenance.
Firstly, vehicle and track vibration characteristics are analyzed based on vehicle-track coupling system model under some kinds of conditions, such as different track condition, different speed, different vehicle types and different track irregularity. The simulation results further show the research significance and provide the data for the latter track irregularity estimation.
A new algorithm based on micro genetic algorithm and vehicle-track coupling system model is proposed in the dissertation, which can acquire the track irregularity using the vehicle dynamic responses of operation train. In the algorithm track static irregularity is taken as an unknown parameter of nonlinear vehicle-track coupling system model, and then the solution of track irregularity is converted into a parameter estimation problem. Making the sum of error square between vehicle track model output and measurement value to be minimum is used as parameter estimation rule, and genetic algorithm is adopted to search the optimum solution in the solution space. Because the dynamic equations of vehicle-track coupling system are the large nonlinear equation set, an improved micro genetic algorithm is studied to reduce the computing time and workload. The restart step is removed from micro genetic algorithm and mutation operator is added. The optimal retention policy is adopted during the evolution process. The estimation results show the estimated track static irregularity error is in the allowable range.
Track stiffness abrupt change caused by track infrastructure defections is the main reason which creates track stiffness irregularity. It can lead to rather large dynamic irregularity when the train goes through it. The dynamic responses of vehicle and track are analyzed when defections exist in the track substructure, and a recognition algorithm of track rigidity abrupt change is proposed based on Support Vector Machines, in which vehicle vibration responses are used. In order to improve the recognition precision, improved SVM is designed by GA and PSO methods to optimize the parameters of SVM. The recognition results show that these two methods are effective to enhance the accuracy and reduce calculation time and get the better results in track substructure defects recognition. Meanwhile, it makes a foundation for the more realistic track model.
A nested algorithm based on GA and Unscented Kalman Filtering is investigated in the dissertation in order to improve the precision of track irregularity estimation, which is influenced by the error of measurement sensors and theoretical model. With the known characteristics of vehicle-track coupling system and measurement sensors, the dynamic responses of track are optimized to get higher estimation precision of track dynamic irregularity. The simulation results show that track static irregularity and all dynamic responses of vehicle and track are getting better after GA and UKF nested algorithm and the estimation error is decreased. Track dynamic irregularity, which operating trains go through at the different speed, can be estimated based on vehicle dynamic responses. The research in this dissertation provides a new solution to detection the track status from the vehicle devices.
引文
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