改进PSO-BP方法在ATP车载设备多故障诊断中的应用研究
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摘要
针对目前ATP车载设备采用单故障诊断方法存在诊断精度偏低的问题,首先对ATP车载设备的故障原因进行分析,并提取出9种故障特征作为输入,7种故障类型作为输出,同时结合ATP车载设备的结构和故障特点建立了改进PSO-BP的多故障诊断模型;其次,在模型求解过程中,引入遗传算法中的变异思想,通过动态调整粒子群的参数来优化BP网络,采用改进PSO-BP算法对此模型进行求解;最后,以武广线数据进行仿真验证,期望输出与实际输出基本一致,故障识别的正确率达到95%。结果表明,采用改进PSO-BP算法解决ATP车载设备的多故障诊断问题是一种有效的方法,其诊断能力优于传统的BP算法和PSO-BP算法。
In order to solve the problem of the single fault diagnostic method with the low efficiency of ATP vehicle equipment, whose cause of fault was analyzed firstly, and the nine fault features were put as input and seven fault types were put as output. At the same time, a multi-fault diagnosis model of improved PSO-BP was established on the basis of the structure with fault characteristics of the equipment. Secondly, in the process of solving the model, the idea of mutation in the genetic algorithm was introduced to optimize the BP network by dynamically adjusting the parameters of the particle swarm optimization( PSO), so the improved PSO-BP algorithm was used to solve the model. Finally, the simulation was carried out with the data of Wuhan-Guangzhou line, the expected and actual output were the same basically, and the correct rate of fault identification reached95%. The results show that it is efficient to use the improved PSO-BP algorithm to solve the multi-fault diagnosis of ATP vehicle equipment, and the diagnostic capability of the method is superior to the traditional BP algorithm and PSO-BP algorithm.
In order to solve the problem of the single fault diagnostic method with the low efficiency of ATP vehicle equipment, whose cause of fault was analyzed firstly, and the nine fault features were put as input and seven fault types were put as output. At the same time, a multi-fault diagnosis model of improved PSO-BP was established on the basis of the structure with fault characteristics of the equipment. Secondly, in the process of solving the model, the idea of mutation in the genetic algorithm was introduced to optimize the BP network by dynamically adjusting the parameters of the particle swarm optimization( PSO), so the improved PSO-BP algorithm was used to solve the model. Finally, the simulation was carried out with the data of Wuhan-Guangzhou line, the expected and actual output were the same basically, and the correct rate of fault identification reached95%. The results show that it is efficient to use the improved PSO-BP algorithm to solve the multi-fault diagnosis of ATP vehicle equipment, and the diagnostic capability of the method is superior to the traditional BP algorithm and PSO-BP algorithm.
引文
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[14] Wan P,Long C J,Huang X M. A detection method of rice process quality based on the color and BP neural network[C]//International Conference on Computer and Computing Technologies in Agriculture. 2010:25-34.
[15] Li B J, Cheng C T. Monthly discharge forecasting using wavelet neural networks with extreme learning machine[J].Science China Technological Sciences,2014,57(12).
[16]田树新,孙胜春,王红霞.基于BP网络的数字电路多故障诊断研究[J].电子器件,2006,29(2):490-492.
[2]曹哲.基于神经网络与案例推理的车载设备故障诊断研究[D].北京:北京交通大学,2016.
[3] Zhao Y,Xu T H,Wang H F. Text mining based fault diagnosis for vehicle on-board equipment of high speed railway signal system[J].Tiedao Xuebao/joumal of the China Railway Society,2015,37(8):53-59.
[4]连可,王厚军,龙兵.基于SVM的模拟电子系统多故障诊断研究[J].仪器仪表学报,2007,28(6):1029-1034.
[5] Kundu,Chattopadhyay S,Sengupta I,et al. Multiple fault diagnosis based on multiple fault simulation using particle swarm optimization[C]//IEEE International Conference on VLSI Design. 2011:364-369.
[6]文天柱,许爱强,程恭.基于改进ENN2聚类算法的多故障诊断方法[J].控制与决策,2015,30(6):1021-1026.
[7] Hui Y, Wang X. The reliability and performability of a repairable and degradable ATP system[C]//Vehicular Technology Conference. 1996:1609-1612.
[8] Hyen B J,Kyu K Y. The study of tilting train installation for ATP(automatic train protection)on-board equipment[C]//IEEE International Conference on Control Automation and Systems. 2010:723-727.
[9]张曙光.CTCS-3级列控系统总体技术方案[M].北京:中国铁道出版社,2008.
[10]中华人民共和国铁道部.CTCS-3级列控车载设备技术规范(暂行):铁运[2012]211号[S].北京:中国铁道出版社,2012.
[11] Liu L,Chen J,Xu L. Realization and application research of BP neural network based on MATLAB[C]//International Seminar on Future Biomedical Information Engineering.IEEE Computer Society,2008:130-133.
[12] Wang Y,Lu C J,Zuo C P. Coal mine safety production forewarning based on improved BP neural network[J]. International Journal of Mining Science and Technology,2015,25(2):319-324.
[13] Ji T L,Lu S F,Tang F,et al. Application of BP neural network model in fracturing productivity prediction of fuyu tight oil reservoir in Jilin oilfield[J].地质学报(英文版),2015,89(A01):154-155.
[14] Wan P,Long C J,Huang X M. A detection method of rice process quality based on the color and BP neural network[C]//International Conference on Computer and Computing Technologies in Agriculture. 2010:25-34.
[15] Li B J, Cheng C T. Monthly discharge forecasting using wavelet neural networks with extreme learning machine[J].Science China Technological Sciences,2014,57(12).
[16]田树新,孙胜春,王红霞.基于BP网络的数字电路多故障诊断研究[J].电子器件,2006,29(2):490-492.
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