基于IBOA-PNN的道岔控制电路故障诊断方法

doi:10.3969/j.issn.1673-4440.2021.12.001

铁路通信信号工程技术 ›› 2021, Vol. 18 ›› Issue (12): 1-6.DOI: 10.3969/j.issn.1673-4440.2021.12.001

• • 下一篇

基于IBOA-PNN的道岔控制电路故障诊断方法

宋丹，汪浩

中车青岛四方车辆研究所有限公司，山东青岛 266011

收稿日期:2021-05-27 修回日期:2021-06-10 出版日期:2021-12-24 发布日期:2021-12-24

Fault Diagnosis Method of Turnout Control Circuit Based on IBOA-PNN

Song Dan, Wang Hao

CRRC Qingdao Sifang Rolling Stock Research Institute Co., Ltd., Qingdao 266011, China

Received:2021-05-27 Revised:2021-06-10 Online:2021-12-24 Published:2021-12-24

摘要/Abstract

摘要： 提出一种基于改进蝴蝶优化算法(Improved Butterfly Optimization Algorithm, IBOA)与概率神经网络(Probabilistic Neural Network，PNN)相结合的诊断方法：在分析得到道岔控制电路典型故障模式和特征的基础上，将IBOA算法与PNN算法相结合，通过改进IBOA算法的强度指数系数对PNN的唯一参数平滑因子进行优化；最后通过IBOA-PNN算法结合采集的道岔电路故障数据对其进行故障诊断。仿真结果表明，此算法运算复杂度低，准确率高，在轨道交通道岔控制电路故障诊断领域具有良好的应用前景

关键词: 道岔控制电路, 故障诊断, 蝴蝶优化算法, 概率神经网络

Abstract: This paper proposes a diagnosis method based on the combination of Improved Butterfly Optimization Algorithm (IBA) and Probabilistic Neural Network (PNN): on the basis of analyzing the typical failure modes and characteristics of the turnout control circuit, the IBOA algorithm is combined with the PNN algorithm, and then the unique parameter smoothing factor of the PNN is optimized by improving the strength index coefficient of the IBOA algorithm; finally, the IBOA-PNN algorithm combined with the collected turnout circuit fault data is used to diagnose the fault. The simulation results show that the algorithm in this paper has low computational complexity and high accuracy, and has a good application prospect in the field of rail transit turnout control circuit fault diagnosis

Key words: turnout control circuit, fault diagnosis, BOA, PNN

中图分类号:

U284.5

宋丹, 汪浩. 基于IBOA-PNN的道岔控制电路故障诊断方法[J]. 铁路通信信号工程技术, 2021, 18(12): 1-6.

Song Dan, Wang Hao. Fault Diagnosis Method of Turnout Control Circuit Based on IBOA-PNN[J]. Railway Signalling & Communication Engineering, 2021, 18(12): 1-6.

图/表 8

参考文献

[1] 赵林海，陆桥.基于灰关联的道岔故障诊断方法[J].铁道学报，2014，36(2):69-74.
Zhao Linhai, Lu Qiao. Method of Turnout Fault Diagnosis Based on Grey Correlation Analysis[J]. Journal of the China Railway Society, 2014, 36(2): 69-74.
[2] 张凯，杜凯，巨永锋.基于BP神经网络的提速道岔故障诊断算法[J].武汉理工大学学报，2014，36(11):77-81.
Zhang Kai, Du Kai, Ju Yongfeng. Algorithm of Speed-up Turnout Fault Diagnosis Based on BP Neural Network[J]. Journal of Wuhan University of Technology, 2014, 36(11): 77-81.
[3] Zhang Kai. The Railway Turnout Fault Diagnosis Algorithm Based on BP Neural Network[C]//2014 IEEE International Conference on Control Science and Systems Engineering. December 29-30, 2014, Yantai, China. IEEE, 2014: 135-138.
[4]董海鹰，李娜.基于D-S证据理论信息融合的转辙机故障诊断方法研究[J].测试技术学报，2013，27(1):1-7.
Dong Haiying, Li Na. Fault Diagnosis Method for Switch Machine Based on D-S Evidence Theory Information Fusion[J]. Journal of Test and Measurement Technology, 2013, 27(1): 1-7.
[5]张友鹏，江雪莹，赵斌.融合粗糙集与灰色模型的道岔故障预测[J].铁道科学与工程学报，2019，16(9):2331-2338.
Zhang Youpeng, Jiang Xueying, Zhao Bin.Turnout Fault Prediction Based on Rough Set and Grey Model[J]. Journal of Railway Science and Engineering, 2019, 16(9): 2331-2338.
[6]王瑞峰，陈旺斌.基于灰色神经网络的S700K转辙机故障诊断方法研究[J].铁道学报，2016，38(6):68-72.
Wang Ruifeng, Chen Wangbin. Research on Fault Diagnosis Method for S700K Switch Machine Based on Grey Neural Network[J]. Journal of the China Railway Society, 2016, 38(6): 68-72.
[7] Arora S, Singh S.Butterfly Optimization Algorithm: A Novel Approach for Global Optimization[J]. Soft Computing, 2019, 23(3): 715-734.
[8] Specht D F.Probabilistic Neural Networks[J]. Neural Networks, 1990, 3(1):109-118.
[9]陈欣.基于粒子群支持向量机的轨道电路故障诊断[J].铁路计算机应用，2016，25(8)：56-58.
Chen Xin.Track Circuit Fault Diagnosis Based on Particle Swarm Optimization and Support Vector Machine[J]. Railway Computer Application, 2016, 25(8): 56-58.

基于IBOA-PNN的道岔控制电路故障诊断方法

Fault Diagnosis Method of Turnout Control Circuit Based on IBOA-PNN

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 8

参考文献

相关文章 2

编辑推荐

Metrics

本文评价

[1]	樊西宁. 道岔控制电路分布电容探讨[J]. 铁路通信信号工程技术, 2022, 19(1): 92-95.
[2]	文武臣. 基于人工智能的ZPW-2000A轨道电路故障诊断方法研究[J]. 铁路通信信号工程技术, 2021, 18(10): 35-39.