叶运广
硕士生导师
个人信息Personal Information
学历:博士研究生毕业
学位:工学博士学位
办公地点:牵引红楼313
性别:男
所在单位:轨道交通运载系统全国重点实验室
报考该导师研究生的方式
欢迎你报考叶运广老师的研究生,报考有以下方式:
1、参加西南交通大学暑期夏令营活动,提交导师意向时,选择叶运广老师,你的所有申请信息将发送给叶运广老师,老师看到后将和你取得联系,点击此处参加夏令营活动
2、如果你能获得所在学校的推免生资格,欢迎通过推免方式申请叶运广老师研究生,可以通过系统的推免生预报名系统提交申请,并选择意向导师为叶运广老师,老师看到信息后将和你取得联系,点击此处推免生预报名
3、参加全国硕士研究生统一招生考试报考叶运广老师招收的专业和方向,进入复试后提交导师意向时选择叶运广老师。
4、如果你有兴趣攻读叶运广老师博士研究生,可以通过申请考核或者统一招考等方式报考该导师博士研究生。
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- Qi Y, Dai H*, Song Y, Ye Y, Li D. Optimisation design of grinding rail profile of metro lines for small radius curved track based on the GFC method: a case study[J]. Vehicle System Dynamics, 2023.
- Ye Y*, Gao H, Huang C, Li H, Shi D, Dai H, Wu P, Zeng J. Computer vision for hunting stability inspection of high-speed trains[J]. Measurement, 2023, 220: 113361.
- Ye Y*, Huang C, Zeng J, Wang S, Liu C, Li F. Predicting railway wheel wear by calibrating existing wear models: Principle and application[J]. Reliability Engineering & System Safety, 2023, 238: 109462.
- Liu C, Song Y*, Li F, Wu P, Ye Y. Stress spectrum compilation method and residual life prediction for hot spot position of metro bogie frame under resonance condition[J]. Engineering Failure Analysis, 2023, 150: 107357.
- Ye Y*, Wei L, Li F, Zeng J, Hecht M. Multislice Time-Frequency Image Entropy as a feature for railway wheel fault diagnosis[J]. Measurement, 2023, 216: 112862.
- Li F*, Yang S, Yuan Z, Shi H, Zeng J, Ye Y. A novel vertical elastic vibration reduction for railway vehicle carbody based on minimum generalized force principle[J]. Mechanical Systems and Signal Processing, 2023, 189.
- Ye Y*, Huang C, Zeng J, Zhou Y, Li F. Shock detection of rotating machinery based on activated time-domain images and deep learning: An application to railway wheel flat detection[J]. Mechanical Systems and Signal Processing, 2023, 186: 109856.
- Ye Y, Zhu B, Huang P*, Peng B. OORNet: A deep learning model for on-board condition monitoring and fault diagnosis of out-of-round wheels of high-speed trains[J]. Measurement, 2022, 199: 111268.
- Shi D*, Šabanovič E, Rizzetto L, Skrickij V, Oliverio R, Kaviani N, Ye Y, Bureika G, Ricci S, Hecht M. Deep learning based virtual point tracking for real-time target-less dynamic displacement measurement in railway applications[J]. Mechanical Systems and Signal Processing, 2022, 166: 108482.
- Ye Y, Huang P*, Zhang Y. Deep learning-based fault diagnostic network of high-speed train secondary suspension systems for immunity to track irregularities and wheel wear[J]. Railway Engineering Science, 2021, 30(1): 96–116.