重载组合列车管路泄漏诊断系统的研究与设计
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摘要
重载组合列年是我国货物运输的重要工具,其制动系统中空气管路的泄漏会严重影响行车安全。空气管路泄漏的诊断和定位是亟待解决的关键问题。论文对管路状态信号进行实时捕捉,并采用多传感器协同定位技术开发研制重载组合列车管路泄漏诊断系统。
以大(同)秦(皇岛)线2万吨重载组合列车为研究对象,在分析列车制动系统的结构、特点和工作原理的基础上,探讨列车管路泄漏的原因和诊断方法,给出重载组合列车管路泄漏诊断系统的整体设计方案。
针对列车管路状态检测中干扰较大、小泄漏难以确定的难点,提出一种信号相关性增强算法捕捉状态信号的奇异点。首先,采用预滤波和自适应滤波方法对状态信号中的干扰和噪声进行滤除；然后利用模2抽取的小波变换将信号分成高频细节和低频概貌,将相邻的高频信号进行乘积运算,增强信号的自相关性,捕捉出管路状态信号在突变时的奇异点,为确定小泄漏的发生时刻奠定基础。
根据列车管路结构复杂、受控源多、控制域长等特点,提出一种多传感器协同迭代定位算法,实现并发多点泄漏的准确定位。采用多个传感器对管路压力和流量联合检测,以区分列车空气管路的正常工况和泄漏状态；根据捕捉到的超长管路负压波信号的奇异点,确定泄漏的相对发生时刻；引入时间校准机制同步多传感器采样时间,在此基础上,把重叠区域的传感器监测信息进行迭代运算,实现并发多泄漏点的准确定位。
最后,采用模块化设计,给出重载组合列车管路泄漏诊断系统的软硬件实现,验证方案和诊断方法的有效性。
The heavy-haul combined train is an important vehicle of the freight transportation in our country. The air pipe leakage of braking system seriously affects the safety operation of trains. The diagnosis and localization of air pipe leakage is the key problem to be solved. In order to develop a diagnosis system for pipe leakage of the heavy-haul combined train, this thesis captures the real-time pipe status signal and uses the multi-sensor co-location technique.
Taking Datong-Qinhuangdao transport line 20,000 tons heavy-haul combined train as the research object, the structure, characteristics and working principle of the braking system are analyzed. By discussing the causes and diagnostic methods of the train pipe leakage, the overall design scheme for the leakage diagnosis system is put forward.
Due to the interference in the monitoring environment and the difficulty in diagnosing small leakage position of the train pipe, this thesis comes up with a signal-correlation-enhanced algorithm of capturing signal singularity. First, the pre-filtering and adaptive filtering techniques can be used to filter the disturbance and noise existing in status signals of the pipe. Then the signal can be divided into the high frequency detail and low frequency profile by using the modulus 2 extracted wavelet transform. The signal's self-correlation can be enhanced by the product operation of the adjacent high-frequency signals. Capturing signal singularity is the basis for determining the time of the occurrence of small leakage.
Because of the complex structure, lots of controlled source, long control domain of train pipe, we propose a multi-sensor cooperative iterative algorithm, which will accurately locate the concurrent multi-spot leakage. In order to diagnose the leakage of train air pipe, multiple sensors are used in the combined detection of pressure and flow. According to the captured singularity of negative pressure wave signal of long pipe, the relative occurrence time of leakage can be determined. The sampling time of multi-sensor can be synchronized by introducing the mechanism of time calibration. On this basis, the location of concurrent multi-spot leakage can be accurately got by the iterative computation of sensors monitoring information in the overlap region.
Finally, this thesis uses the modular design concept. The implementation of software and hardware of the diagnosis system is achieved. The effectiveness of the scheme and diagnostic method is verified.
以大(同)秦(皇岛)线2万吨重载组合列车为研究对象,在分析列车制动系统的结构、特点和工作原理的基础上,探讨列车管路泄漏的原因和诊断方法,给出重载组合列车管路泄漏诊断系统的整体设计方案。
针对列车管路状态检测中干扰较大、小泄漏难以确定的难点,提出一种信号相关性增强算法捕捉状态信号的奇异点。首先,采用预滤波和自适应滤波方法对状态信号中的干扰和噪声进行滤除；然后利用模2抽取的小波变换将信号分成高频细节和低频概貌,将相邻的高频信号进行乘积运算,增强信号的自相关性,捕捉出管路状态信号在突变时的奇异点,为确定小泄漏的发生时刻奠定基础。
根据列车管路结构复杂、受控源多、控制域长等特点,提出一种多传感器协同迭代定位算法,实现并发多点泄漏的准确定位。采用多个传感器对管路压力和流量联合检测,以区分列车空气管路的正常工况和泄漏状态；根据捕捉到的超长管路负压波信号的奇异点,确定泄漏的相对发生时刻；引入时间校准机制同步多传感器采样时间,在此基础上,把重叠区域的传感器监测信息进行迭代运算,实现并发多泄漏点的准确定位。
最后,采用模块化设计,给出重载组合列车管路泄漏诊断系统的软硬件实现,验证方案和诊断方法的有效性。
The heavy-haul combined train is an important vehicle of the freight transportation in our country. The air pipe leakage of braking system seriously affects the safety operation of trains. The diagnosis and localization of air pipe leakage is the key problem to be solved. In order to develop a diagnosis system for pipe leakage of the heavy-haul combined train, this thesis captures the real-time pipe status signal and uses the multi-sensor co-location technique.
Taking Datong-Qinhuangdao transport line 20,000 tons heavy-haul combined train as the research object, the structure, characteristics and working principle of the braking system are analyzed. By discussing the causes and diagnostic methods of the train pipe leakage, the overall design scheme for the leakage diagnosis system is put forward.
Due to the interference in the monitoring environment and the difficulty in diagnosing small leakage position of the train pipe, this thesis comes up with a signal-correlation-enhanced algorithm of capturing signal singularity. First, the pre-filtering and adaptive filtering techniques can be used to filter the disturbance and noise existing in status signals of the pipe. Then the signal can be divided into the high frequency detail and low frequency profile by using the modulus 2 extracted wavelet transform. The signal's self-correlation can be enhanced by the product operation of the adjacent high-frequency signals. Capturing signal singularity is the basis for determining the time of the occurrence of small leakage.
Because of the complex structure, lots of controlled source, long control domain of train pipe, we propose a multi-sensor cooperative iterative algorithm, which will accurately locate the concurrent multi-spot leakage. In order to diagnose the leakage of train air pipe, multiple sensors are used in the combined detection of pressure and flow. According to the captured singularity of negative pressure wave signal of long pipe, the relative occurrence time of leakage can be determined. The sampling time of multi-sensor can be synchronized by introducing the mechanism of time calibration. On this basis, the location of concurrent multi-spot leakage can be accurately got by the iterative computation of sensors monitoring information in the overlap region.
Finally, this thesis uses the modular design concept. The implementation of software and hardware of the diagnosis system is achieved. The effectiveness of the scheme and diagnostic method is verified.
引文
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[2]冀彬.大秦线重载列车发展研究.中国铁路,2009,(03)：31～35
[3]耿志修.大秦线开行两万吨重载组合列车系统集成与创新.中国铁路,2007,(09)：25～29
[4]俎以宏,张中央.机车制动状态微机检测控制系统的研究与应用.内燃机车,2007,8：20～22
[5]魏伟.两万吨组合列车制动特性.交通运输工程学报,2007,7(6)：12～16
[6]Zhang P.C. Development of locomotive auto-passing neutral section device based on onboard monitoring device. China Railway Science,2009,3(20):141～144
[7]李兴华,黄志武,杨迎泽,李烁.基于多智能体的组合列车同步制动系统故障诊断.计算机测量与控制,2009,17(9)：1676～1678,1687
[8]侯文明.HXD1型机车制动系统故障在线诊断技术的研究与应用：[硕士学位论文].长沙：中南大学,2010
[9]Zakharov S.M., Goryachevab I., Bogdanova V., et al. Wheels and rail profiles optimization for heavy haul lines in Russia. International Heavy Haul Association 2007 Specialist Technical Session-High Tech in Heavy Haul,2007,289-294
[10]Mille A.R., Peters J., Smith B.E., et al. Analysis of fuel cell hybrid locomotives. Journal of Power Sources,2006,157(2):855～61
[11]姚永康.日本机车车辆电子设备故障的分析与对策.电力机车与城轨车辆,2007,5：51～53
[12]Toffolo A. Fuzzy expert systems for the diagnosis of component and sensor faults in complex energy systems. Journal of Energy Resources Technology-Transactions of the ASME,2009,131 (4):237～241
[13]缆接ECP制动系统——性能要求AARS_4200 标准
[14]Cowin A. Application of radio-controlled ECP braking system on heavy haul freight train. Foreign Rolling Stock,2003,40(6):10-13
[15]冯双洲.ICE3和ICE-T—德国铁路新一代动车组.变流技术与电力牵引,2001,1：28～33
[16]吴国栋,李碧波.法国TGV的发展历史和技术特点.国外铁道车辆,2007,44(1)：1-4
[17]松本耕辅.车辆实时监控系统研制.国外铁道车辆,2008,45(5)：42～44
[18]刘泉,高殿柱,陈爱军.电空制动机故障诊断系统设计.电力机车与城轨车辆,2007,30(6)：8-11
[19]周强强,魏良，彭军.基于Lonworks和FPGA的CCBⅡ状态监测系统设计.科技广场,2008,12：182～184
[20]李烁.和谐Ⅰ型电力机车制动系统监测单元的设计与实现：[硕士学位论文].长沙：中南大学,2009
[21]丁建波,欧长劲,骆芳.JZ-7型机车制动机故障诊断系统.内燃机车,2007,(8)：27～29
[22]范玉刚,李平,宋执环.基于特征样本的KPCA在故障诊断中的应用.控制与决策,2005,20(12)：1415-1418
[23]王华伟,郭世明.机车故障诊断系统显示装置.研究与开发,2008,27(5)：23～25
[24]罗仁,曾京,戴焕云.列车系统建模及运行平稳性分析.中国铁道科学,2006,27(1)：72～77
[25]周忠良,马大炜,王成国,康熊.货物列车充风过程中空气流量变化的试验研.中国铁道科学,2008,29(1)：76～81
[26]Zakharov S.M., Goryachevab I., Bogdanova V., et al. Wheels and rail profiles optimization for heavy haul lines in Russia. International Heavy Haul Association 2007 Specialist Technical Session-High Tech in Heavy Haul,2007,289-294
[27]Hiroshi T., Izumi H., Kazuyuki K., et al. A Study on Characteristic of Pressure Reduction of Compressed Air in a Lon g Pipe. Nippon Kikai Gakkai Rombunshu B Hen,1986,52 (481):3181-3185
[28]Vanosdol J., Parsons J., Edward L. Using staged compresion and expansion to enhance the performance of a gas turbine fuel cell hybrid system, In:Proc 7th Annual International Conference on Fuel Cell Science, Engineering, and Technology, Newport Beach, CA, United states. June 2009.559-571
[29]廖洪涛.和谐HXD1型大功率交流电力机车概述.电力机车与城轨车辆,2007,1：7-10
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