Deep learning-based fault diagnostic network of high-speed train secondary suspension systems for immunity to track irregularities and wheel wear

IF 4.4 1区 工程技术 Q2 TRANSPORTATION SCIENCE & TECHNOLOGY Railway Engineering Science Pub Date : 2021-10-20 DOI:10.1007/s40534-021-00252-z
Ye, Yunguang, Huang, Ping, Zhang, Yongxiang
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引用次数: 1

Abstract

Fault detection and isolation of high-speed train suspension systems is of critical importance to guarantee train running safety. Firstly, the existing methods concerning fault detection or isolation of train suspension systems are briefly reviewed and divided into two categories, i.e., model-based and data-driven approaches. The advantages and disadvantages of these two categories of approaches are briefly summarized. Secondly, a 1D convolution network-based fault diagnostic method for high-speed train suspension systems is designed. To improve the robustness of the method, a Gaussian white noise strategy (GWN-strategy) for immunity to track irregularities and an edge sample training strategy (EST-strategy) for immunity to wheel wear are proposed. The whole network is called GWN-EST-1DCNN method. Thirdly, to show the performance of this method, a multibody dynamics simulation model of a high-speed train is built to generate the lateral acceleration of a bogie frame corresponding to different track irregularities, wheel profiles, and secondary suspension faults. The simulated signals are then inputted into the diagnostic network, and the results show the correctness and superiority of the GWN-EST-1DCNN method. Finally, the 1DCNN method is further validated using tracking data of a CRH3 train running on a high-speed railway line.

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基于深度学习的高速列车二次悬挂系统抗轨道不平整和车轮磨损故障诊断网络
高速列车悬挂系统的故障检测与隔离对保证列车运行安全至关重要。首先,简要回顾了现有的列车悬挂系统故障检测或隔离方法,并将其分为基于模型的方法和数据驱动的方法两大类。简要总结了这两类方法的优缺点。其次,设计了一种基于一维卷积网络的高速列车悬挂系统故障诊断方法。为了提高该方法的鲁棒性,提出了高斯白噪声策略(gwn -)和边缘样本训练策略(est -)来抵抗车轮磨损。整个网络称为GWN-EST-1DCNN方法。第三,为验证该方法的有效性,建立了高速列车的多体动力学仿真模型,对不同轨道不平直度、车轮型线和二次悬架故障对应的转向架框架进行横向加速度仿真。将仿真信号输入到诊断网络中,结果表明了GWN-EST-1DCNN方法的正确性和优越性。最后,利用高速铁路上运行的CRH3列车的跟踪数据进一步验证了1DCNN方法。
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来源期刊
Railway Engineering Science
Railway Engineering Science TRANSPORTATION SCIENCE & TECHNOLOGY-
CiteScore
10.80
自引率
7.90%
发文量
1061
审稿时长
15 weeks
期刊介绍: Railway Engineering Science is an international, peer-reviewed, and free open-access journal that publishes original research articles and comprehensive reviews related to fundamental engineering science and emerging technologies in rail transit systems, focusing on the cutting-edge research in high-speed railway, heavy-haul railway, urban rail transit, maglev system, hyperloop transportation, etc. The main goal of the journal is to maintain high quality of publications, serving as a medium for railway academia and industry to exchange new ideas and share the latest achievements in scientific research, technical innovation and industrial development in railway science and engineering. The topics include but are not limited to Design theory and construction technology System dynamics and safetyElectrification, signaling and communicationOperation and maintenanceSystem health monitoring and reliability Environmental impact and sustainabilityCutting-edge technologiesThe publication costs for Railway Engineering Science are fully covered by Southwest Jiaotong University so authors do not need to pay any article-processing charges.
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