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On the Polygonal Wear Evolution of Heavy-Haul Locomotive Wheels due to Wheel/Rail Flexibility and Its Mitigation Measures

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Chinese Journal of mechanical Engineering 2024年第1期37卷 40-61页

作者： Yunfan Yang Feifan Chai Pengfei Liu Liang Ling Kaiyun Wang Wanming Zhai State Key Laboratory of mechanical Behavior and system safety of Traffic Engineering Structures Shijiazhuang Tiedao UniversityShijiazhuang 050043China State Key Laboratory of Rail Transit Vehicle system Southwest Jiaotong UniversityChengdu 610031China

Wheel polygonal wear can immensely worsen wheel/rail interactions and vibration performances of the train and track,and ultimately,lead to the shortening of service life of railway components.At present,wheel/rail medium-or high-frequency frictional interactions are perceived as an essential reason of the high-order polygonal wear of railway wheels,which are potentially resulted by the flexible deformations of the train/track system or other external excitations.In this work,the effect of wheel/rail flexibility on polygonal wear evolution of heavy-haul locomotive wheels is explored with aid of the long-term wheel polygonal wear evolution simulations,in which different flexible modeling of the heavy-haul wheel/rail coupled system is implemented.Further,the mitigation measures for the polygonal wear of heavy-haul locomotive wheels are discussed.The results point out that the evolution of polygonal wear of heavy-haul locomotive wheels can be veritably simulated with consideration of the flexible effect of both wheelset and rails.Execution of mixed-line operation of heavy-haul trains and application of multicut wheel re-profiling can effectively reduce the development of wheel polygonal wear.This research can provide a deep-going understanding of polygonal wear evolution mechanism of heavy-haul locomotive wheels and its mitigation measures.

关键词： Heavy-haul locomotive Wheel polygonal wear Wheel/rail flexibility Long-term polygonal wear evolution Mitigation measures

Dynamic analysis of axle box bearings on the high-speed train caused by wheel-rail excitation

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Applied Mathematics and Mechanics(English Edition) 2024年第3期45卷 441-460页

作者： Qiaoying MA Shaopu YANG Yongqiang LIU Baosen WANG Zechao LIU State Key Laboratory of mechanical behavior and system safety of Traffic Engineering Structures Shijiazhuang Tiedao UniversityShijiazhuang 050043China School of Transportation Shijiazhuang Tiedao UniversityShijiazhuang 050043China School of mechanical Engineering Shijiazhuang Tiedao UniversityShijiazhuang 050043China

To explore the impact of wheel-rail excitation on the dynamic performance of axle box bearings,a dynamic model of the high-speed train including axle box bearings is developed.Subsequently,the dynamic response characteristics of the axle box bearing are examined.The investigation focuses on the acceleration characteristics of bearing vibration under excitation of track irregularities and wheel flats.In addition,experiments on both normal and faulty bearings are conducted separately,and the correctness of the model and some conclusions are verified.According to the research,track irregularity is unfavorable for bearing fault detection based on resonance demodulation.Under the same speed conditions,the acceleration peak of bearing is inversely proportional to the length of the wheel flat and directly proportional to its depth.The paper will contribute to a deeper understanding of the dynamic performance of axle box bearings.

关键词： high-speed train track irregularity wheel flat dynamic simulation

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Weighted Heterogeneous Graph-Based Incremental Automatic Disease Diagnosis Method

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Journal of Shanghai Jiaotong university(Science) 2024年第1期29卷 120-130页

作者：田圆圆金衍瑞李志远刘金磊刘成良 State Key Laboratory of mechanical system and Vibration School of Mechanical EngineeringShanghai Jiao Tong UniversityShanghai 200240China

The objective of this study is to construct a multi-department symptom-based automatic diagnosis model.However,it is dificult to establish a model to classify plenty of diseases and collect thousands of disease-symptom datasets simultaneously.Inspired by the thought of"knowledge graph is model",this study proposes to build an experience-infused knowledge model by continuously learning the experiential knowledge from data,and incrementally injecting it into the knowledge graph.Therefore,incremental learning and injection are used to solve the data collection problem,and the knowledge graph is modeled and containerized to solve the large-scale multi-classification problems.First,an entity linking method is designed and a heterogeneous knowledge graph is constructed by graph fusion.Then,an adaptive neural network model is constructed for each dataset,and the data is used for statistical initialization and model training.Finally,the weights and biases of the learned neural network model are updated to the knowledge graph.It is worth noting that for the incremental process,we consider both the data and class increments.We evaluate the diagnostic effectiveness of the model on the current dataset and the anti-forgetting ability on the historical dataset after class increment on three public datasets.Compared with the classical model,the proposed model improves the diagnostic accuracy of the three datasets by 5%,2%,and 15%on average,respectively.Meanwhile,the model under incremental learning has a better ability to resist forgetting.

关键词： knowledge graph disease diagnosis incremental learning adaptive neural network knowledge model

Space truss construction modeling based on on-orbit assembly motion feature

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Chinese Journal of Aeronautics 2024年第3期37卷 365-379页

作者： Changjie ZHAO Weizhong GUO Meng CHEN Jiaxing HU Youcheng HAN Rongfu LIN State Key Lab of mechanical system and Vibration Shanghai Jiao Tong UniversityShanghai 200240China Institute of Aerospace system Engineering Shanghai Shanghai 201109China

More space truss construction has been planned to develop and utilize space resources.These trusses are designed in the way of large-scale,complex,modular,and on-orbit assembly.To meet the upcoming challenge of large-scale space infrastructure construction,it is necessary to study space truss automation design and robotic construction.This paper proposes an ordinal finite screw adjacency matrix model(OFSAMM),focusing on the relationship between assembly motions,to express and compute a space truss structure.In this model,a space truss is abstracted as a set of ordered assembly motions,each of which is recorded as a finite screw as the basic element of the truss and its assembly.The operation of truss transformation is also derived under this model.Therefore,the truss configuration,the assembly sequence,the truss sub-assembly,the truss components,and the on-orbit assembly task can be expressed and calculated in a unified model,which is calculated and stores the truss topology and assembly with the minimum storage cost.At the end of this paper,we introduce how to synthesize and optimize space truss design through two cases.The study will help to improve design efficiency.Furthermore,it provides a theoretical basis for the automatic construction of space truss structures,especially in the next stage.

关键词： Adjacency matrix Finite screw On-orbit construction modeling Space applications Space trusses Topology design

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Unsupervised construction of health indicator for rotating machinery via multi-criterion feature selection and attentive variational autoencoder

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Science China(Technological Sciences) 2024年第5期67卷 1524-1537页

作者： LI XinYu CHENG ChangMing PENG ZhiKe State Key Laboratory of mechanical system and Vibration Shanghai Jiao Tong UniversityShanghai200240China School of mechanical Engineering Ningxia UniversityYinchuan750021China

Health indicator(HI)construction is a crucial task in degradation evaluation and facilitates the prognostic and health management(PHM)of rotating machinery.Excluding interference from artificial labeling,the HI construction approaches in an unsupervised manner have attracted substantial attention.Nevertheless,current unsupervised methods generally struggle with two problems:(1)ignorance of both redundancy between features and global variability of features during the feature selection process;(2)inadequate utilization of information from different sampling moments.To tackle these problems,this work develops a novel unsupervised approach for HI construction that integrates multi-criterion feature selection and the Attentive Variational Autoencoder(Attentive VAE).Explicitly,a multi-criterion feature selection(Mc FS)algorithm together with an elaborately designed metric is proposed to determine a superior feature subset,considering the relevance,the redundancy,and the global variability of features simultaneously.Then,for the adequate utilization of the information from distinct sampling moments,a deep learning model named Attentive VAE is established.The Attentive VAE is solely fed with the selected features in the health state for model training and the HI is derived through the reconstruction error to reveal the degradation degree of machinery.Two case studies based on genuine experimental datasets are involved to quantitatively evaluate the superiority of the developed approach,demonstrating its superiority over other unsupervised methods for characterizing degradation processes.The effectiveness of both the Mc FS algorithm and the Attentive VAE is verified by ablation experiments,respectively.

关键词： health indicator(HI) unsupervised learning multi-criterion feature selection global variability attention mechanism

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Prediction of contact resistance of electrical contact wear using different machine learning algorithms

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Friction 2024年第6期12卷 1250-1271页

作者： Zhen-bing CAI Chun-lin LI Lei YOU Xu-dong CHEN Li-ping HE Zhong-qing CAO Zhi-nan ZHANG Tribology research Institute Southwest Jiaotong UniversityChengdu 610031China State Key Laboratory of mechanical system and Vibration Shanghai Jiao Tong UniversityShanghai 200240China

H62 brass material is one of the important materials in the process of electrical energy transmission and signal transmission,and has excellent performance in all aspects.Since the wear behavior of electrical contact pairs is particularly complex when they are in service,we evaluated the effects of load,sliding velocity,displacement amplitude,current intensity,and surface roughness on the changes in contact resistance.Machine learning(ML)algorithms were used to predict the electrical contact performance of different factors after wear to determine the correlation between different factors and contact resistance.Random forest(RF),support vector regression(SVR)and BP neural network(BPNN)algorithms were used to establish RF,SVR and BPNN models,respectively,and the experimental data were trained and tested.It was proved that BP neural network model could better predict the stable mean resistance of H62 brass alloy after wear.Characteristic analysis shows that the load and current have great influence on the predicted electrical contact properties.The wear behavior of electrical contacts is influenced by factors such as load,sliding speed,displacement amplitude,current intensity,and surface roughness during operation.Machine learning algorithms can predict the electrical contact performance after wear caused by these factors.Experimental results indicate that an increase in load,current,and surface roughness leads to a decrease in stable mean resistance,while an increase in displacement amplitude and frequency results in an increase in stable mean resistance,leading to a decline in electrical contact performance.To reduce testing time and costs and quickly obtain the electrical contact performance of H62 brass alloy after wear caused by different factors,three algorithms(random forest(RF),support vector regression(SVR),and BP neural network(BPNN))were used to train and test experimental results,resulting in a machine learning model suitable for predicting the stable mean resistance of H62 brass alloy after wear.The pr

关键词： electrical contact wear contact resistance machine learning(ML) neural network

Probabilistic-Ellipsoid Hybrid Reliability Multi-Material Topology Optimization Method Based on Stress Constraint

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Computer Modeling in Engineering & Sciences 2024年第7期140卷 757-792页

作者： Zibin Mao Qinghai Zhao Liang Zhang College of mechanical and Electrical Engineering Qingdao UniversityQingdao266071China National Engineering research Center for Intelligent Electrical Vehicle Power system Qingdao UniversityQingdao266071China

This paper proposes a multi-material topology optimization method based on the hybrid reliability of the probability-ellipsoid model with stress constraint for the stochastic uncertainty and epistemic uncertainty of mechanical loads in optimization design.The probabilistic model is combined with the ellipsoidal model to describe the uncertainty of mechanical loads.The topology optimization formula is combined with the ordered solid isotropic material with penalization(ordered-SIMP)multi-material interpolation model.The stresses of all elements are integrated into a global stress measurement that approximates the maximum stress using the normalized p-norm function.Furthermore,the sequential optimization and reliability assessment(SORA)is applied to transform the original uncertainty optimization problem into an equivalent deterministic topology optimization(DTO)problem.Stochastic response surface and sparse grid technique are combined with SORA to get accurate information on the most probable failure point(MPP).In each cycle,the equivalent topology optimization formula is updated according to the MPP information obtained in the previous cycle.The adjoint variable method is used for deriving the sensitivity of the stress constraint and the moving asymptote method(MMA)is used to update design variables.Finally,the validity and feasibility of the method are verified by the numerical example of L-shape beam design,T-shape structure design,steering knuckle,and 3D T-shaped beam.

关键词： Stress constraint probabilistic-ellipsoid hybrid topology optimization reliability analysis multi-material design

Combined effect of WS2 and Ti3C2Tx MXene favors oil-based ultra-low friction on rough steel-steel interface at elevated temperatures

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Science China Technological Sciences 2024年第7期67卷 1991-2002页

作者： LIU YangQin CHEN Lei WANG Yang QIAN LinMao Tribology research Institute State Key Laboratory of Rail Transit Vehicle System School of Mechanical EngineeringSouthwest Jiaotong University Key Laboratory of Advanced Technologies of Materials (Ministry of Education) Research Institute of Frontier ScienceSouthwest Jiaotong University

The lubrication performance of liquids is severely restricted and is degraded in high-temperature environments. Stable and reliable lubrication in high temperature environments has been a long-standing goal in various industrial fields. In this study,WS2and Ti3C2TxMXene nanoflakes were used as oil-based lubricant additives to generate ultra-low friction and even superlubricity（friction coefficient of ～0.007） at elevated temperatures（400°C）, which has hitherto not been achieved by both individual pristine materials, WS2and Ti3C2TxMXene. Viscosity and thermogravimetric characterization revealed improvements in the high-temperature rheological properties and thermal stability of the lubricating base oil, indicating improved loadbearing and continuous lubrication capabilities at elevated temperatures. X-ray photoelectron spectroscopy, transmission electron microscopy, and atomic force microscopy demonstrated that the formation of an iron/titanium/tungsten-rich oxide lubricious thin film at the sliding interface reduced the interfacial shear stress, which was responsible for the observed friction and wear reductions at high contact pressures（> 1.1 GPa）. Although the titanium/tungsten oxide film was gradually removed after prolonged sliding, a sufficiently thick iron oxide film maintained a low friction coefficient for at least 2 h. The improved surface quality facilitates the achievement of ultra-low friction and reduced wear. The proposed lubrication methodology has a broad utilization potential as a wear-reduction strategy across various industrial fields at elevated temperatures.

关键词： ultra-low friction high-temperature wear reduction lubricating additive tungsten disulfide MXene

Anticipating the lifespan: Predicting the durability of an anode-supported solid oxide fuel cell short stack over 50,000 h

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Progress in Natural Science:Materials International 2024年第3期34卷 606-613页

作者： Muhammad Zubair Khan Amjad Hussain Seung-Bok Lee Tak-Hyoung Lim Rak-Hyun Song Department of Materials Science & Engineering Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology Mang Haripur Hydrogen Energy research division Korea Institute of Energy Research Department of Advanced Energy and system Engineering Korea University of Science and Technology (UST)

In the present study, the operational lifetime of a solid oxide fuel(SOFC) short stack is predicted by investigating the performance degradation of both the short stack and its cells throughout 1000 h at 800°C. The short stack and integral cell voltages are continuously measured during the long-term test, with electrochemical impedance spectroscopy(EIS) conducted every 200 h. The short stack voltage decreased rapidly for the initial 200–300 h and afterwards, it decreased at a slow rate due to the increase in the Ohmic and polarization resistances in the same manner. Scanning electron microscopy results show that there is no delamination or cracking among constituent layers of the short-stack cells. The single degradation effects of the Ni coarsening in the anode, cation migration and surface segregation in cathode and oxide scale growth in metallic interconnect mesh are successfully integrated into a comprehensive lifetime prediction model. The experimentally measured voltage degradation data of the short stack fits well with the developed mathematical model and allows the successful prediction of the lifetime up to 50,000 h.

关键词： Degradation Impedance Lifetime SOFC Stack