Rotary-scaling fine-tuning (RSFT) method for optimizing railway wheel profiles and its application to a locomotive

The existing multi-objective wheel profile optimization methods mainly consist of three sub-modules:(1)wheel profile generation,(2)multi-body dynamics simulation,and(3)an optimization algorithm.For the first module,a comparably conservative rotary-scaling finetuning(RSFT)method,which introduces two design variables and an empirical formula,is proposed to fine-tune the traditional wheel profiles for improving their engineering applicability.For the second module,for the TRAXX locomotives serving on the Blankenburg–Rubeland line,an optimization function representing the relationship between the wheel profile and the wheel–rail wear number is established based on Kriging surrogate model(KSM).For the third module,a method combining the regression capability of KSM with the iterative computing power of particle swarm optimization(PSO)is proposed to quickly and reliably implement the task of optimizing wheel profiles.Finally,with the RSFT–KSM–PSO method,we propose two wear-resistant wheel profiles for the TRAXX locomotives serving on the Blankenburg–Rubeland line,namely S1002-S and S1002-M.The S1002-S profile minimizes the total wear number by 30%,while the S1002-M profile makes the wear distribution more uniform through a proper sacrifice of the tread wear number,and the total wear number is reduced by 21%.The quasi-static and hunting stability tests further demonstrate that the profile designed by the RSFT–KSM–PSO method is promising for practical engineering applications.

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

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 highspeed 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-1 DCNN 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.

Railway wheel profile fine-tuning system for profile recommendation

This paper develops a wheel profile fine-tuning system(WPFTS)that comprehensively considers the influence of wheel profile on wheel damage,vehicle stability,vehicle safety,and passenger comfort.WPFTS can recommend one or more optimized wheel profiles according to train operators’needs,e.g.,reducing wheel wear,mitigating the development of wheel out-of-roundness(OOR),improving the shape stability of the wheel profile.Specifically,WPFTS includes four modules:(I)a wheel profile generation module based on the rotary-scaling finetuning(RSFT)method;(II)a multi-objective generation module consisting of a rigid multi-body dynamics simulation(MBS)model,an analytical model,and a rigid–flexible MBS model,for generating 11 objectives related to wheel damage,vehicle stability,vehicle safety,and passenger comfort;(III)a weight assignment module consisting of an adaptive weight assignment strategy and a manual weight assignment strategy;and(IV)an optimization module based on radial basis function(RBF)and particle swarm optimization(PSO).Finally,three cases are introduced to show how WPTFS recommends a wheel profile according to train operators’needs.Among them,a wheel profile with high shape stability,a wheel profile for mitigating the development of wheel OOR,and a wheel profile considering hunting stability and derailment safety are developed,respectively.

加速碳中和的能源载体和动力总成技术

由于“巴黎协定”和中国碳中和的气候保护目标及对能源独立的追求,需要加速对节能减排和二氧化碳中和的技术研究与开发。目前,在汽车动力系统的节能减排与碳中和方面,主要有3种发展概念:电池驱动;燃料电池驱动;基于二氧化碳中和的内燃机可持续合成燃料驱动。根据能源使用的生命周期,将效率因素简化归纳为决策因素,在时间上无法反映出减少二氧化碳排放及减少化石能源的需求。与必要的能源供应相比,可再生能源的区域可用性有限,因此须以成本效益优势的方式来解决化石能源大规模替代的技术与基础建设问题。除了直接使用可再生电力外,绿色氢气和衍生的合成燃料可以大大加快化石能源的替代。短期内,合成燃料可以实施到全球的现有车辆上(约12亿辆),并持续增加混合汽油、柴油或天然气的混合比例,直到化石能源完全被绿色的氢气、合成燃料或电力所取代。介绍了氢气动力系统(H2内燃机和燃料电池动力系统)与合成燃料相关的技术解决方案。

Quantification of the influence of rolling stock failures on track deterioration

This study focuses on the quantification of the influence of rolling stock failures(RSFs)on railway infrastructure.Taking the wheel flat,a common RSF,as an example,we introduce four quantification indexes to evaluate the influence on the following four deterioration mechanisms:track settlement(TS),track component fatigue(TCF),abrasive wear(AW),and rolling contact fatigue(RCF).Our results indicate that TS,TCF,and AW increase sharply with the increase of the wheel flat length and the vehicle speed,and this increasing trend becomes more acute with the increase of the wheel flat length and the vehicle speed.At low speeds,RCF increases gradually as the wheel flat length increases;at high speeds,it increases sharply at first and then decreases gradually.The influence of the wheel flat on TCF and AW is the most obvious,followed by TS and RCF.These findings can help infrastructure managers(IMs)to better understand infrastructure conditions related to RSFs and can aid them in managing problems with vehicle abnormality in track access charging.

Erratum to:Quantification of the influence of rolling stock failures on track deterioration

The original version of this article unfortunately contained a mistake.In line 6,p.785,the data“0.06”should be“6.04×10^-6”,i.e.“The results showed that among these seven failures,the occurrence probabilities of the wheel out-of-round and the wheel flat are the highest,both are approximately 6.04×10^-6.”