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张顶成

 
 

姓名: 张顶成

职称:副教授

导师情况:硕士生导师

电话:

传真:

     邮箱:dc_zhang@scu.edu.cn

招生方向:机械信号处理与分析、机械设备状态监测与运行维护、大数据驱动的智能故障诊断、机械设备的剩余寿命预测

教育背景及工作经历

教育背景

2016.09–2020.11  伯明翰大学(博士) 导师:Clive Roberts& Edward Stewart

2013.09–2016.06  湖南大学(硕士) 导师:于德介教授

2009.09–2013.06  湖南工学院(学士)


工作经历:

2021.03-至今     四川大学,副教授

2020.10-2021.03  香港城市大学(博士后)合作导师:谢旻 教授

2020.09-2020.10  清华大学(访问学者)合作导师:李勇、李彦夫 教授

2020.07-2020.09  西安交通大学(访问学者)合作导师:雷亚国 教授

总体介绍
 

主要研究工作:

目前主要研究机械设备故障诊断、剩余寿命预测与健康管理,所涉及领域包括无损检测、信号处理、机器学习、多传感融合和维护策略。参与中英交流项目1项、国家自然科学基金委项目1项以及香港特别行政区大学拨款委员会项目1项。近年来在IEEE Transactions on Instrumentation and Measurement、Journal of Sound and Vibration、Measurement、振动工程学报等国内外权威期刊发表论文13篇,并担任多个国际期刊审稿人和国际会议主持人。


学术兼职:

以下期刊同行评审:

Mechanical Systems and Signal Processing》《IEEE Transactions on Instrumentation and Measurement》《IEEE Sensors Journal》《Measurement》《Part F: Journal of Rail and Rapid Transit》IEEE Access》《Entropy》《Computers and Industrial Engineering》《Journal of Sound and Vibration》等


会议主持:

· IEEE Global Reliability and Prognostics & Health Management Conference, 2020, Shanghai, China

· IEEE International Conference on Industrial Engineering and Engineering Management, 2020, Online

开设课程
 
研究领域及在研项目
 

· 英国皇家学会与国家自然科学基金委员会,中英交流项目,5181102049,高铁关键部件的健康状态深度识别与预测技术研究,2019-03至2022-03,20万元,在研,参与

· 香港特别行政区大学拨款委员会,T32-101/15-R,高速铁路与铁道系统的安全性、可靠性和应急管理研究,2016-01至2021-12,3500万(港币),在研,参与

· 国家自然科学基金委员会,面上项目,R-IND5904,产品质量保证策略与质保服务运作研究,2016-01至2020-12,80万,已结题,参与

授权专利
 
发表论文

[1] Ye J, Stewart E, Zhang D*, Chen Q, Thangaraj K, Roberts C. Integration of Multiple Sensors for Non-Contact Rail Profile Measurement and Inspection[J]. IEEE Transactions on Instrumentation & Measurement. 2021, 70: 1-12

[2] Zhang D*, Entezami M, Stewart E, Roberts C, Yu D, Lei Y. Wayside Acoustic Detection of Train Bearings Based on An Enhanced Spline-Kernelled Chirplet Transform[J]. Journal of Sound and Vibration, 2020, 480, 115401.

[3] Zhang D*, Stewart E, Entezami M, Roberts C, Yu D. Intelligent Acoustic-based Fault Diagnosis   of Roller Bearings Using a Deep Graph Convolutional Network[J]. Measurement, 2020,156, 107585.

[4] Zhang D*, Stewart E, Ye J, Entezami M, Roberts C. Roller Bearing Degradation Assessment Based on a Deep MLP Convolution Neural Network Considering Outlier Regions[J]. IEEE Transactions on Instrumentation & Measurement. 2020, 69(6): 2996-3004.

[5] Ye J, Stewart E, Zhang D*, Chen Q, Roberts C. A Method for Automatic Railway Track Surface Defect Classification and Evaluation Using a Laser-based 3D Model[J]. IET Image Processing. 2020, 14(12): 2701-2710

[6] Zhang D*, Entezami M, Stewart E, Roberts C, Yu D. A Novel Doppler Effect Reduction Method for Wayside Acoustic Train Bearing Fault Detection Systems[J]. Applied Acoustics, 2019, 145: 112-124.

[7] Zhang D*, Entezami M, Stewart E, Roberts C, Yu D. Adaptive Fault Feature Extraction from Wayside Acoustic Signals from Train Bearings[J]. Journal of Sound and Vibration, 2018, 425, 221-238.

[8] Zhang D, Yu D *. Multi-fault diagnosis of gearbox based on resonance-based signal sparse decomposition and comb filter[J]. Measurement, 2017, 103, 361-369.

[9] Zhang D, Yu D*, Li X. Optimal resonance-based signal sparse decomposition and its application to fault diagnosis of rotating machinery [J]. ARCHIVE Proceedings of the Institution of Mechanical Engineers Part C: Journal of Mechanical Engineering Science. 2016, 231, 4670-4683.

[10] Zhang D, Yu D*, Zhang W. Energy operator demodulating of optimal resonance components for the compound faults diagnosis of gearboxes. Measurement Science and Technology, 26(11), 2015,115003

[11] Zhang D*, Stewart E, Entezami M, Roberts C. Degradation Assessment of Bearings Using Deep Convolutional Inner-Ensemble Learning with Outlier Removal[C]. 2019 Prognostics and System Health Management Conference,2019:315-319.

[12]张顶成,于德介*,李星. 滚动轴承故障诊断的可调品质因子可调小波重构方法[J]. 航空动力学报,2015,30(12), 3051-3057.

[13]李星,于德介*,张顶成. 基于最优品质因子信号共振稀疏分解的滚动轴承故障诊断[J].振动工程学报,2015,2015(6), 998-1005


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